Technical Writing in Your Discipline(Speech Language and Pathology SLP)

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For this project, you will be completing preliminary research on technical writing in your discipline.Select 5 journals from the list provided below (or beyond it, if your discipline isnt represented on the list, or you want to find something else!) and find at least 5 total articles that talk about the role technical communication and/or technical writing within your discipline. Then, write a paper on the patterns you found regarding the audience, context, purpose of technical writing in your discipline, and anything new you learned about your discipline. Cite all research in the citation format used on your discipline, both in your own writing throughout the paper, and in an appropriate reference section at the conclusion. Five articles related to AAC are attached,you can look for your own articles related to AAC or use the 5 articles attached. Also the guidlines for the paper is also attached.

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AJSLP Tutorial Brain–Computer Interfaces for Augmentative and Alternative Communication: A Tutorial Jonathan S. Brumberg,a Kevin M. Pitt,b Alana Mantie-Kozlowski,c and Jeremy D. Burnisond Purpose: Brain–computer interfaces (BCIs) have the potential to improve communication for people who require but are unable to use traditional augmentative and alternative communication (AAC) devices. As BCIs move toward clinical practice, speech-language pathologists (SLPs) will need to consider their appropriateness for AAC intervention. Method: This tutorial provides a background on BCI approaches to provide AAC specialists foundational knowledge necessary for clinical application of BCI. Tutorial descriptions were generated based on a literature review of BCIs for restoring communication. Results: The tutorial responses directly address 4 major areas of interest for SLPs who specialize in AAC: (a) the current state of BCI with emphasis on SLP scope of practice (including the subareas: the way in which individuals access AAC with BCI, the efficacy of BCI for AAC, and the effects of fatigue), (b) populations for whom BCI is best suited, (c) the future of BCI as an addition to AAC access strategies, and (d) limitations of BCI. Conclusion: Current BCIs have been designed as access methods for AAC rather than a replacement; therefore, SLPs can use existing knowledge in AAC as a starting point for clinical application. Additional training is recommended to stay updated with rapid advances in BCI. I & Baker, 2012). In the most serious cases of total paralysis with loss of speech (e.g., locked-in syndrome; Plum & Posner, 1972), even these advanced methods are not sufficient to provide access to language and literacy (Oken et al., 2014). Access to communication is critical for maintaining social interactions and autonomy of decision-making in this population (Beukelman & Mirenda, 2013); therefore, individuals with paralysis and akinetic mutism have been identified as potential candidates for brain–computer interface (BCI) access to AAC (Fager et al., 2012). BCIs for communication take AAC and access technology to the next level and provide a method for selecting and constructing messages by detecting changes in brain activity for controlling communication software (Wolpaw, Birbaumer, McFarland, Pfurtscheller, & Vaughan, 2002). In particular, they are devices that provide a direct link between an individual and a computer device through brain activity alone, without requiring any overt movement or behavior. As an access technique, BCIs have the potential to reduce or eliminate some physical barriers to successful AAC intervention for individuals with severe speech and physical impairments. Similar to AAC and associated access techniques, current BCI technology can take a variety of forms on the basis of the neural signal targeted and the method used for individuals to interact with the communication ndividuals with severe speech and physical impairments often rely on augmentative and alternative communication (AAC) and specialized access technologies to facilitate communication on the basis of the nature and severity of their speech, motor, and cognitive impairments. In some cases, people who use AAC are able to use specially modified computer peripherals (e.g., mouse, joystick, stylus, or button box) to access AAC devices, whereas in other, more severe cases, sophisticated methods are needed to detect the most subtle of movements (e.g., eye gaze tracking; Fager, Beukelman, Fried-Oken, Jakobs, a Department of Speech-Language-Hearing: Sciences and Disorders, Neuroscience Graduate Program, The University of Kansas, Lawrence b Department of Speech-Language-Hearing: Sciences and Disorders, The University of Kansas, Lawrence c Communication Sciences and Disorders Department, Missouri State University, Springfield d Neuroscience Graduate Program, The University of Kansas, Lawrence Correspondence to Jonathan S. Brumberg: Editor-in-Chief: Krista Wilkinson Editor: Erinn Finke Received December 31, 2016 Revision received April 6, 2017 Accepted August 14, 2017 Disclosure: The authors have declared that no competing interests existed at the time of publication. American Journal of Speech-Language Pathology • Vol. 27 • 1–12 • February 2018 • Copyright © 2018 American Speech-Language-Hearing Association 1 interface. Each of these factors may impose different demands on the cognitive and motor abilities of individuals who use BCI (Brumberg & Guenther, 2010). Although the field of BCI has grown over the past decade, many stakeholders including speech-language pathologists (SLPs), other practitioners, individuals who use AAC and potentially BCI, and caretakers are unfamiliar with the technology. SLPs are a particularly important stakeholder given their role as the primary service providers who assist clients with communicative challenges secondary to motor limitations through assessment and implementation of AAC interventions and strategies. A lack of core knowledge on the potential use of BCI for clinical application may limit future intervention with BCI for AAC according to established best practices. This tutorial will offer some basic explanations regarding BCI, including the benefits and limitations of this access technique, and the different varieties of BCI. It will also provide a description of individuals who may be potentially best suited for using BCI to access AAC. An understanding of this information is especially important for SLPs specializing in AAC who are most likely to interact with BCI as they move from research labs into real-world situations (e.g., classrooms, home, work). Tutorial Descriptions by Topic Area Topic 1: How Do People Who Use BCI Interact With the Computer? BCIs are designed to allow individuals to control computers and communication systems using brain activity alone and are separated according to whether signals are recorded noninvasively from/through the scalp or invasively through implantation of electrodes in or on the brain. Noninvasive BCIs, those that are based on brain recordings made through the intact skull without requiring a surgical procedure (e.g., electroencephalography or EEG, magnetoencephalography, functional magnetic resonance imaging, functional near-infrared spectroscopy), often use an indirect technique to map brain signals unrelated to communication onto controls for a communication interface (Brumberg, Burnison, & Guenther, 2016). Though there are many signal acquisition modalities for noninvasive recordings of brain activity, noninvasive BCIs typically use EEG, which is recorded through electrodes placed on the scalp according to a standard pattern (Oostenveld & Praamstra, 2001) and record voltage changes that result from the simultaneous activation of millions of neurons. EEG can be analyzed for its spontaneous activity, or in response to a stimulus (e.g., event-related potentials), and both have been examined for indirect access BCI applications. In contrast, another class of BCIs attempts to directly output speech from imagined/attempted productions (Blakely, Miller, Rao, Holmes, & Ojemann, 2008; Brumberg, Wright, Andreasen, Guenther, & Kennedy, 2011; Herff et al., 2015; Kellis et al., 2010; Leuthardt et al., 2011; Martin et al., 2014; Mugler et al., 2014; Pei, Barbour, Leuthardt, & Schalk, 2011; Tankus, Fried, & Shoham, 2012); however, these 2 techniques typically rely on invasively recorded brain signals (via implanted microelectrodes or subdural electrodes) related to speech motor preparation and production. Though in their infancy, direct BCIs for communication have the potential to completely replace the human vocal tract for individuals with severe speech and physical impairments (Brumberg, Burnison, & Guenther, 2016; Chakrabarti, Sandberg, Brumberg, & Krusienski, 2015); however, the technology does not yet provide a method to “read thoughts.” For the remainder of this tutorial, we focus on noninvasive, indirect methods for accessing AAC with BCIs, and we refer readers to other sources for descriptions of direct BCIs for speech (Brumberg, Burnison, & Guenther, 2016; Chakrabarti et al., 2015). Indirect methods for BCI parallel other access methods for AAC devices, where nonspeech actions (e.g., button press, direct touch, eye gaze) are translated to a selection on a communication interface. The main difference between the two access methods is that BCIs rely on neurophysiological signals related to sensory stimulation, preparatory motor behaviors, and/or covert motor behaviors (e.g., imagined or attempted limb movements), rather than overt motor behavior used for conventional access. The way in which individuals control a BCI greatly depends on the neurological signal used by the device to make selections on the communication interface. For instance, in the case of an eye-tracking AAC device, one is required to gaze at a communication icon, and the system makes a selection on the basis of the screen coordinates of the eye gaze location. For a BCI, individuals may be required to (a) attend to visual stimuli to generate an appropriate visual–sensory neural response to select the intended communication icon (e.g., Donchin, Spencer, & Wijesinghe, 2000), (b) take part in an operant conditioning paradigm using biofeedback of EEG (e.g., Kübler et al., 1999), (c) listen to auditory stimuli to generate auditory–sensory neural responses related to the intended communication output (e.g., Halder et al., 2010), or (d) imagine movements of the limbs to alter the sensorimotor rhythm (SMR) to select communication items (e.g., Pfurtscheller & Neuper, 2001). At present, indirect BCIs are more mature as a technology, and many have already begun user trials (Holz, Botrel, Kaufmann, & Kübler, 2015; Sellers, Vaughan, & Wolpaw, 2010). Therefore, SLPs are most likely to be involved with indirect BCIs first as they move from the research lab to the real world. Indirect BCI techniques are very similar to current access technologies for high-tech AAC; for example, the output of the BCI system can act as an input method for conventional AAC devices. Below, we review indirect BCI techniques and highlight their possible future in AAC. The P300-Based BCI The visual P300 grid speller (Donchin et al., 2000) is the most well-known and most mature technology with ongoing at-home user trials (Holz et al., 2015; Sellers et al., 2010). Visual P300 BCIs for communication use the P300 event-related potential, a neural response to novel, rare American Journal of Speech-Language Pathology • Vol. 27 • 1–12 • February 2018 visual stimuli in the presence of many other visual stimuli, to select items on a communication interface. The traditional graphical layout for a visual P300 speller is a 6 × 6 grid that includes the 26 letters of the alphabet, space, backspace, and numbers (see Figure 1). Each row and column1 on the spelling grid are highlighted in a random order, and a systematic variation in the EEG waveform is generated when one attends to a target item for selection, the “oddball stimulus,” which occurs infrequently compared with the remaining items (Donchin et al., 2000). The event-related potential in response to the target item will contain a positive voltage fluctuation approximately 300 ms after the item is highlighted (Farwell & Donchin, 1988). The BCI decoding algorithm then selects items associated with detected occurrences of the P300 for message creation (Donchin et al., 2000). The P300 grid speller has been operated by individuals with amyotrophic lateral sclerosis (ALS; Nijboer, Sellers, et al., 2008; Sellers & Donchin, 2006) and has been examined as part of at-home trials by individuals with neuromotor impairments (Holz et al., 2015; Sellers & Donchin, 2006), making it a likely candidate for future BCI-based access for AAC. In addition to the cognitive requirements for operating the P300 speller, successful operation depends somewhat on the degree of oculomotor control (Brunner et al., 2010). Past findings have shown that the P300 amplitude can be reduced if individuals are unable to use an overt attention strategy (gazing directly at the target) and, instead, must use a covert strategy (attentional change without ocular shifting), which can degrade BCI performance (Brunner et al., 2010). An alternative P300 interface displays a single item at a time on the screen (typically to the center as in Figure 1, second from left) to alleviate concerns for individuals with poor oculomotor control. This interface, known as the rapid serial visual presentation speller, has been successfully controlled by a cohort of individuals across the continuum of locked-in syndrome severity (Oken et al., 2014). All BCIs that use spelling interfaces require sufficient levels of literacy, though many can be adapted to use icon or symbol-based communication (e.g., Figure 2). Auditory stimuli can also be used to elicit P300 responses for interaction with BCI devices for individuals with poor visual capability (McCane et al., 2014), such as severe visual impairment, impaired oculomotor control, and cortical blindness. Auditory interfaces can also be used in poor viewing environments, such as outdoors or in the presence of excessive lighting glare. Like its visual counterpart, the auditory P300 is elicited via an oddball paradigm, and has been typically limited to binary (yes/no) selection by attending to one of two different auditory tones presented monaurally to each ear (Halder et al., 2010), or linguistic stimuli (e.g., attending to a “yep” target among “yes” presentations in the right ear vs. “nope” and “no” in the left; Hill et al., 2014). The binary control achieved 1 Each individual item may also be highlighted, rather than rows and columns. using the auditory P300 interface has the potential to be used to navigate a spelling grid similar to conventional auditory scanning techniques for accessing AAC systems, by attending to specific tones that correspond to rows and columns (Käthner et al., 2013; Kübler et al., 2009). There is evidence that auditory grid systems may require greater attention than their visual analogues (Klobassa et al., 2009; Kübler et al., 2009), which should be considered when matching clients to the most appropriate communication device. Steady State Evoked Potentials BCIs can be controlled using attention-modulated steady state brain rhythms, as opposed to event-related potentials, in both visual (steady state visually evoked potential [SSVEP]) and auditory (auditory steady state response [ASSR]) domains. Both the SSVEP and ASSR are physiological responses to a driving input stimulus that are amplified when an individual focuses his or her attention on the stimulus (Regan, 1989). Strobe stimuli are commonly used for SSVEP, whereas amplitude-modulated tones are often used for ASSR (Regan, 1989). BCIs using SSVEP exploit the attention-modulated response to strobe stimuli by simultaneously presenting multiple communication items for selection, each flickering at a different frequency (Cheng, Gao, Gao, & Xu, 2002; Friman, Luth, Volosyak, & Graser, 2007; Müller-Putz, Scherer, Brauneis, & Pfurtscheller, 2005).2 As a result, all item flicker rates will be observed in the EEG recordings, but the frequency of the attended stimulus will contain the largest amplitude (Lotte, Congedo, Lécuyer, Lamarche, & Arnaldi, 2007; Müller-Putz et al., 2005; Regan, 1989) and greatest temporal correlation to the strobe stimulus (Chen, Wang, Gao, Jung, & Gao, 2015; Lin, Zhang, Wu, & Gao, 2007). The stimulus with the greatest neurophysiological response will then be selected by the BCI to construct a message, typically via an alphanumeric keyboard (shown in Figure 1), though icons can be adapted for different uses and levels of literacy (e.g., Figure 2). Major advantages of this type of interface are the following: (a) high accuracy rates, often reported above 90% with very little training (e.g., Cheng et al., 2002; Friman et al., 2007); (b) overlapping, centrally located stimuli could be used for individuals with impaired oculomotor control (Allison et al., 2008). A major concern with this technique, however, is an increased risk for seizures (Volosyak, Valbuena, Lüth, Malechka, & Gräser, 2011). BCIs that use the ASSR require one to shift his or her attention to a sound stream that contains a modulated stimulus (e.g., a right monoaural 38-Hz amplitude modulation, 1000-Hz carrier tone presented with a left monoaural 42-Hz modulated, 2500-Hz carrier; Lopez, Pomares, Pelayo, Urquiza, & Perez, 2009). As with the SSVEP, the modulation frequency of the attended sound stream is 2 There are other variants that use a single flicker rate with a specific strobe pattern that is beyond the scope of this tutorial. Brumberg et al.: AAC-BCI Tutorial 3 Figure 1. From left to right, example visual displays for the following BCIs: P300 grid speller, RSVP P300, SSVEP, and motor-based (SMR with keyboard). For the P300 grid, each row and column are highlighted until a letter is selected. In the RSVP, each letter is displayed randomly, sequentially in the center of the screen. For the SSVEP, this example uses four flickering stimuli (at different frequencies) to represent the cardinal directions, which are used to select individual grid items. This can also be done with individual flicker frequencies for all 36 items with certain technical considerations. For the motor-based BCI, this is an example of a binary-selection virtual keyboard; imagined right hand movements select the right set of letters. RSVP = rapid serial visual presentation; SSVEP = steady state visually evoked potential; SMR = sensorimotor rhythm; BCI = brain–computer interfaces. Copyright © Tobii Dynavox. Reprinted with permission. observable in the recorded EEG signal and will be amplified relative to the other competing stream. Therefore, in this example, if the BCI detects the greatest EEG amplitude at 38 Hz, it will perform a binary action associated with the right-ear tone (e.g., yes or “select”), whereas detection of the greatest EEG amplitude at 42 Hz will generate a left-ear tone action (e.g., no or “advance”). Motor-Based BCIs Another class of BCIs provides access to communication interfaces using changes in the SMR, a neurological signal related to motor production and motor imagery (Pfurtscheller & Neuper, 2001; Wolpaw et al., 2002), for individuals with and without neuromotor impairments (Neuper, Müller, Kübler, Birbaumer, & Pfurtscheller, 2003; Vaughan et al., 2006). The SMR is characterized by the μ (8–12 Hz) and β (18–25 Hz) band spontaneous EEG oscillations that are known to desynchronize, or reduce in amplitude, during covert and overt movement attempts (Pfurtscheller & Neuper, 2001; Wolpaw et al., 2002). Many motor-based BCIs use left and right limb movement imagery because the SMR desynchronization will occur on the contralateral side, and are most often used to control spelling interfaces (e.g., virtual keyboard: Scherer, Müller, Neuper, Graimann, & Pfurtscheller, 2004; DASHER: Wills & MacKay, 2006; hex-o-spell: Blankertz et al., 2006; see Figure 1, right, for an example), though they can be used as inputs to commercial AAC devices as well (Brumberg, Burnison, & Pitt, 2016). Two major varieties of motor-based BCIs have been developed for controlling computers: those that provide continuous cursor control (analogous to mouse/joystick and eye gaze) and others that use discrete selection (analogous to button presses). An example layout of keyboard-based and symbol-based motor-BCI interfaces are shown in Figures 1 and 2. Cursor-style BCIs transform changes in the SMR continuously over time into computer control signals (Wolpaw & McFarland, 2004). One example of a continuous, SMR-based BCI uses imagined movements of the hands and feet to move a cursor to select progressively refined Figure 2. From left to right, examples of how existing BCI paradigms can be applied to page sets from current AAC devices: P300 grid, SSVEP, motor based (with icon grid). For the P300 grid interface, a row or column is highlighted until a symbol is selected (here, it is yogurt). For the SSVEP, either directional (as shown here) or individual icons flicker at specified strobe rates to either move a cursor or directly select an item. For motor based, the example shown here uses attempted or imagined left hand movements to advance the cursor and right hand movements to choose the currently selected item. SSVEP = steady state visually evoked potential; SMR = sensorimotor rhythm; BCI = brain–computer interfaces; AAC = augmentative and alternative communication. Copyright © Tobii Dynavox. Reprinted with permission. 4 American Journal of Speech-Language Pathology • Vol. 27 • 1–12 • February 2018 groups of letters organized at different locations around a computer screen (Miner, McFarland, & Wolpaw, 1998; Vaughan et al., 2006). Another continuous-style BCI is used to control the “hex-o-spell” interface in which imagined movements of the right hand rotate an arrow to point at one of six groups of letters, and imagined foot movements extend the arrow to select the current letter group (Blankertz et al., 2006). Discrete-style motor BCIs perform this transformation using the event-related desynchronization (Pfurtscheller & Neuper, 2001), a change to the SMR in response to some external stimulus, like an automatically highlighted row or column via scanning interface. One example of a discrete-style motor BCI uses the event-related desynchronization to control a virtual keyboard consisting of a binary tree representation of letters, in which individuals choose between two blocks of letters, selected by (imagined) right or left hand movements until a single letter or item remains (Scherer et al., 2004). Most motor-based BCIs require many weeks or months for successful operation and report accuracies greater than 75% for individuals without neuromotor impairments and, in one study, 69% accuracy for individuals with severe neuromotor impairments (Neuper et al., 2003). Motor-based BCIs are inherently independent from interface feedback modality because they rely only on an individual’s ability to imagine his or her limbs moving, though users are often given audio or visual feedback of BCI choices (e.g., Nijboer, Furdea, et al., 2008). A recent, continuous motor BCI has been used to produce vowel sounds with instantaneous auditory feedback by using limb motor imagery to control a two-dimensional formant frequency speech synthesizer (Brumberg, Burnison, & Pitt, 2016). Other recent discrete motor BCIs have been developed for row–column scanning interfaces (Brumberg, Burnison, & Pitt, 2016; Scherer et al., 2015). 2003). Nearly all BCIs require some amount of cognitive effort or selective attention, though the amount of each depends greatly on the style and modality of the specific device. Individuals with other neuromotor disorders, such as cerebral palsy, muscular dystrophies, multiple sclerosis, Parkinson’s disease, and brain tumors, may require AAC (Fried-Oken, Mooney, Peters, & Oken, 2013; Wolpaw et al., 2002) but are not yet commonly considered for BCI studies and interventions (cf. Neuper et al., 2003; Scherer et al., 2015), due to concomitant impairments in cognition, attention, and memory. In other instances, elevated muscle tone and uncontrolled movements (e.g., spastic dysarthria, dystonia) limit the utility of BCI due to the introduction of physical and electromyographic movement artifacts (i.e., muscle-based signals that are much stronger than EEG and can distort recordings of brain activity). BCI research is now beginning to consider important human factors involved in appropriate use of BCI for individuals (FriedOken et al., 2013) and for coping with difficulties in brain signal acquisition due to muscular (Scherer et al., 2015) and environmental sources of artifacts. Developing BCI protocols to help identify the BCI technique most appropriate for each individual must be considered as BCI development moves closer to integration with existing AAC techniques. Topic 2: Who May Best Benefit From a BCI? BCI Summary BCIs use a wide range of techniques for mapping brain activity to communication device control through a combination of signals related to sensory, motor, and/or cognitive processes (see Table 1 for a summary of BCI types). The choice of BCI protocol and feedback methods trade off with cognitive abilities needed for successful device operation (e.g., Geronimo, Simmons, & Schiff, 2016; Kleih & Kübler, 2015; Kübler et al., 2009). Many BCIs require individuals to follow complex, multistep procedures and require potentially high levels of attentional capacity that are often a function of the sensory or motor process used for BCI operation. For example, the P300 speller BCI (Donchin et al., 2000) requires that individuals have an ability to attend to visual stimuli and make decisions about them (e.g., recognize the intended visual stimulus among many other stimuli). BCIs that use SSVEPs depend on the neurological response to flickering visual stimuli (Cheng et al., 2002) that is modulated by attention rather than other cognitive tasks. These two systems both use visual stimuli to elicit neural activity for controlling a BCI but differ in their demands on cognitive and attention processing. In contrast, motor-based BCI systems (e.g., Pfurtscheller & Neuper, 2001; Wolpaw et al., 2002) require individuals to have sufficient motivation and volition, as well as an ability to learn how changing mental tasks can control a communication device. At present, BCIs are best suited for individuals with acquired neurological and neuromotor impairments leading to paralysis and loss of speech with minimal cognitive involvement (Wolpaw et al., 2002), for example, brainstem stroke and traumatic brain injury (Mussa-Ivaldi & Miller, Sensory, Motor, and Cognitive Factors Alignment of the sensory, motor, and cognitive requirements for using BCI to access AAC devices with individuals’ unique profile will help identify and narrow down Operant Conditioning BCIs This interface operates by detecting a stimulusindependent change in brain activity, which is used to select options on a communication interface. The neural signals used for controlling the BCI are not directly related to motor function or sensation. Rather, it uses EEG biofeedback for operant conditioning to teach individuals to voluntarily change the amplitude and polarity of the slow cortical potential, a slow-wave (< 1 Hz) neurological rhythm that is related to movements of a one-dimensional cursor. In BCI applications, cursor vertical position is used to make binary selections for communication interface control (Birbaumer et al., 2000; Kübler et al., 1999). Brumberg et al.: AAC-BCI Tutorial 5 Table 1. Summary of BCI varieties and their feedback modality. EEG signal type Event-related potentials Sensory/Motor modality User requirements Visual P300 (grid) Visual P300 (RSVP) Auditory P300 Steady state evoked potentials Steady state visually evoked potential Motor-based Auditory steady state response Continuous sensorimotor rhythm Discrete event-related desynchronization Operant conditioning Motor preparatory signals, for example, contingent negative variation Slow cortical potentials Visual oddball paradigm, requires selective attention around the screen Visual oddball paradigm, requires selective attention to the center of the screen only (poor oculomotor control) Auditory oddball paradigm, requires selective auditory attention, no vision requirement Attention to frequency tagged visual stimuli, may increase seizure risk Attention to frequency modulated audio stimuli Continuous, smooth control of interface (e.g., cursors) using motor imagery (first person) Binary (or multichoice) selection of interface items (# choices = # of imagined movements), requires motor imagery ability Binary selection of communication interface items using imagined movements Binary selection of communication interface items after biofeedback-based learning protocol Note. BCI = brain–computer interface; EEG = electroencephalography; RSVP = rapid serial visual presentation. the number of candidate BCI variants (e.g., feature matching; Beukelman & Mirenda, 2013; Light & McNaughton, 2013), which is important for improving user outcomes with the chosen device (Thistle & Wilkinson, 2015). Matching possible BCIs should also include overt and involuntary motor considerations, specifically the presence of spasticity or variable muscle tone/dystonia, which may produce electromyographic artifacts that interfere with proper BCI function (Goncharova, McFarland, Vaughan, & Wolpaw, 2003). In addition, there may be a decline in brain signals used for BCI decoding as symptoms of progressive neuromotor diseases become more severe (Kübler, Holz, Sellers, & Vaughan, 2015; Silvoni et al., 2013) that may result in decreased BCI performance. The wide range in sensory, motor, and cognitive components of BCI designs point to a need for user-centered design frameworks (e.g., Lynn, Armstrong, & Martin, 2016) and feature matching/screening protocols (e.g., Fried-Oken et al., 2013; Kübler et al., 2015), like those used for current practices in AAC intervention (Light & McNaughton, 2013; Thistle & Wilkinson, 2015). Topic 3: Are BCIs Faster Than Other Access Methods for AAC? Current AAC devices yield a range of communication rates that depend on access modality (e.g., direct selection, scanning), level of literacy, and information represented by each communication item (e.g., single-meaning icons or images, letters, icons representing complex phrases; Hill & Romich, 2002; Roark, Fried-Oken, & Gibbons, 2015), as well as word prediction software (Trnka, McCaw, Yarrington, McCoy, & Pennington, 2008). Communication rates using AAC are often less than 15 words per minute (Beukelman & Mirenda, 2013; Foulds, 1980), and slower speeds (two to 6 five words per minute; Patel, 2011) are observed for letter spelling due to the need for multiple selections for spelling words (Hill & Romich, 2002). Word prediction and language modeling can increase both speed and typing efficiency (Koester & Levine, 1996; Roark et al., 2015; Trnka et al., 2008), but the benefits may be limited due to additional cognitive demands (Koester & Levine, 1996). Scan rate in auto-advancing row–column scanning access methods also affects communication rate, and though faster scan rates should lead to faster communication rates, slower scan rates can reduce selection errors (Roark et al., 2015). BCIs are similarly affected by scan rate (Sellers & Donchin, 2006); for example, a P300 speller can only operate as fast as each item is flashed. Increases in flash rate may also increase cognitive demands for locating desired grid items while ignoring others, similar to effects observed using commercial AAC visual displays (Thistle & Wilkinson, 2013). Current BCIs for communication generally yield selection rates that are slower than existing AAC methods, even with incorporation of language prediction models (Oken et al., 2014). Table 2 provides a summary of selection rates from recent applications of conventional access techniques and BCI to communication interfaces. Both individuals with and without neuromotor impairments using motor-based BCIs have achieved selection rates under 10 selections (letters, numbers, symbols) per minute (Blankertz et al., 2006; Neuper et al., 2003; Scherer et al., 2004), and those using P300 methods commonly operate below five selections per minute (Acqualagna & Blankertz, 2013; Donchin et al., 2000; Nijboer, Sellers, et al., 2008; Oken et al., 2014). A recent P300 study using a novel presentation technique has obtained significantly higher communication rates of 19.4 characters per minute, though the method has not been studied in detail with participants American Journal of Speech-Language Pathology • Vol. 27 • 1–12 • February 2018 Table 2. Communication rates from recent BCI and conventional access to communication interfaces. BCI method Population Selection rate Berlin BCI (motor imagery) Graz BCI (motor imagery) Graz BCI (motor imagery) P300 speller (visual) Healthy Healthy Impaired Healthy P300 speller (visual) RSVP P300 RSVP P300 ALS ALS LIS Healthy 2.3–7.6 letters/min 2.0 letters/min 0.2–2.5 letter/min 4.3 letters/min 19.4 char/min (120.0 bits/min) 1.5–4.1 char/min (4.8–19.2 bits/min) 3–7.5 char/min 0.4–2.3 char/min 1.2–2.5 letter/min SSVEP Healthy AAC (row–column) Healthy LIS Healthy AAC (direct selection) 33.3 char/min 10.6 selections/min (27.2 bits/min) 18–22 letters/min 6.0 letters/min 5.2 words/min Source Blankertz et al. (2006) Scherer et al. (2004) Neuper et al. (2003) Donchin et al. (2000) Townsend and Platsko (2016) Nijboer, Sellers, et al. (2008) Mainsah et al. (2015) Oken et al. (2014) Acqualagna and Blankertz (2013), Oken et al. (2014) Chen et al. (2015) Friman et al. (2007) Roark et al. (2015) Trnka et al. (2008) Note. BCI = brain–computer interface; ALS = amyotrophic lateral sclerosis; RSVP = rapid serial visual presentation; LIS = locked-in syndrome; SSVEP = steady state visually evoked potential; AAC = augmentative and alternative communication; char = character. with neuromotor impairments (Townsend & Platsko, 2016). BCIs, on the basis of the SSVEP, have emerged as a promising technique often yielding both high accuracy (> 90%) and communication rates as high as 33 characters per minute (Chen et al., 2015). From these reports, BCI performance has started to approach levels associated with AAC devices using direct selection, and the differences in communication rates for scanning AAC devices and BCIs (shown in Table 2) are reduced when making comparisons between individuals with neuromotor impairments rather than individuals without impairments (e.g., AAC: six characters per minute; Roark et al., 2015; BCI: one to eight characters per minute; Table 2). Differences in communication rate can also be reduced based on the type of BCI method (e.g., 3–7.5 characters per minute; Mainsah et al., 2015). These results suggest that BCI has become another clinical option for AAC intervention that should be considered during the clinical decision-making process. BCIs have particular utility when considered for the most severe cases; the communication rates described in the literature are sufficient to provide access to language and communication for those who are currently without both. Recent improvements in BCI designs have shown promising results (e.g., Chen et al., 2015; Townsend & Platsko, 2016), which may start to push BCI communication efficacy past current benchmarks for AAC. Importantly, few BCIs have been evaluated over extended periods of time (Holz et al., 2015; Sellers et al., 2010); therefore, it is possible that BCI selection may improve over time with training. Topic 4: Fatigue and Its Effects BCIs, like conventional AAC access techniques, require various levels of attention, working memory, and cognitive load that all affect the amount of effort (and fatigue) needed Table 3. Take-home points collated from the interdisciplinary research team that highlight the major considerations for BCI as possible access methods for AAC. BCIs do not yet have the ability to translate thoughts or speech plans into fluent speech productions. Direct BCIs, usually involving a surgery for implantation of recording electrodes, are currently being developed as speech neural prostheses. Noninvasive BCIs are most often designed as an indirect method for accessing AAC, whether custom developed or commercial. There are a variety of noninvasive BCIs that can support clients with a range of sensory, motor, and cognitive abilities—and selecting the most appropriate BCI technique requires individualized assessment and feature matching procedures. The potential population of individuals who may use BCIs is heterogeneous, though current work is focused on individuals with acquired neurological and neuromotor disorders (e.g., locked-in syndrome due to stroke, traumatic brain injury, and ALS); limited study has involved individuals with congenital disorders such as CP. BCIs are currently not as efficient as existing AAC access methods for individuals with some form of movement, though the technology is progressing. For these individuals, BCIs provide an opportunity to augment or complement existing approaches. For individuals with progressive neurodegenerative diseases, learning to use BCI before speech and motor function worsen beyond the aid of existing access technologies may help maintain continuity of communication. For those who are unable to use current access methods, BCIs may provide the only form of access to communication. Long-term BCI use is only just beginning; BCI performance may improve as the technology matures and as individuals who use BCI gain greater proficiency and familiarity with the device. Note. BCI = brain–computer interface; AAC = augmentative and alternative communication; ALS = amyotrophic lateral sclerosis; CP = cerebral palsy. Brumberg et al.: AAC-BCI Tutorial 7 to operate the device (Kaethner, Kübler, & Halder, 2015; Pasqualotto et al., 2015). There is evidence that scanningtype AAC devices are not overly tiring (Gibbons & Beneteau, 2010; Roark, Beckley, Gibbons, & Fried-Oken, 2013), but prolonged AAC use can have a cumulative effect and reduce communication effectiveness (Trnka et al., 2008). In these cases, language modeling and word prediction can reduce fatigue and maintain high communication performance using an AAC device (Trnka et al., 2008). Within BCI, reports of fatigue, effort, and cognitive load are mixed. Individuals with ALS have reported that visual P300 BCIs required more effort and time compared with eye gaze access (Pasqualotto et al., 2015), whereas others reported that a visual P300 speller was easier to use, and not overly exhausting compared with eye gaze, because it does not require precise eye movements (Holz et al., 2015; Kaethner et al., 2015). Other findings from these studies indicate that the visual P300 speller incurred increased cognitive load and fatigue for some (Kaethner et al., 2015), whereas for others, there is less strain compared to eye-tracking systems (Holz et al., 2015). The application of many conventional and BCI-based AAC access techniques with the same individual may permit an adaptive strategy to rely on certain modes of access based on each individual’s level of fatigue. This will allow one to change his or her method of AAC access to suit his or her fatigue level throughout the day. Topic 5: BCI as an Addition to Conventional AAC Access Technology At their current stage of development, BCIs are mainly the primary choice for individuals with either absent, severely impaired, or highly unreliable speech and motor control. As BCIs advance as an access modality for AAC, it is important that the goal of intervention remains on selecting an AAC method that is most appropriate versus selecting the most technologically advanced access method (Light & McNaughton, 2013). Each of the BCI devices discussed has unique sensory, motor, and cognitive requirements that may best match specific profiles of individuals who may require BCI, as well as the training required for device proficiency. The question then of BCIs replacing any form of AAC must be determined according to the needs, wants, and abilities of the individual. These factors play a crucial role on motivation, which has direct impact on BCI effectiveness (Nijboer, Birbaumer, & Kübler, 2010). Other assessment considerations include comorbid conditions, such as a history of seizures, which is a contraindication for some visual BCIs due to the rapidly flashing icons (Volosyak et al., 2011). Cognitive factors, such as differing levels of working memory (Sprague, McBee, & Sellers, 2015) and an ability to focus one’s attention (Geronimo et al., 2016; Riccio et al., 2013), are also important considerations because they have been correlated to successful BCI operation. There are additional considerations for motor-based BCIs, including (a) a well-known observation that the SMR, which is necessary for device control, cannot be adequately 8 estimated in approximately 15%–30% of all individuals with or without impairment (Vidaurre & Blankertz, 2010) and (b) the possibility of performance decline or instability as a result of progressive neuromotor disorders, such as ALS (Silvoni et al., 2013). These concerns are currently being addressed using assessment techniques to predict motor-based BCI performance, including a questionnaire to estimate kinesthetic motor imagery (e.g., first person imagery or imagining performing and experiencing the sensations associated with motor imagery) performance (Vuckovic & Osuagwu, 2013), which is known to lead to better BCI performance compared with a third person motor imagery (e.g., watching yourself from across the room; Neuper, Scherer, Reiner, & Pfurtscheller, 2005). Overall, there is limited research available on the inter- and intraindividual considerations for BCI intervention that may affect BCI performance (Kleih & Kübler, 2015); therefore, clinical assessment tools and guidelines must be developed to help determine the most appropriate method of accessing AAC (that includes both traditional or BCI-based technologies) for each individual. These efforts have already begun (e.g., Fried-Oken et al., 2013; Kübler et al., 2015), and more work is needed to ensure that existing AAC practices are well incorporated with BCI-based assessment tools. In summary, the ultimate purpose of BCI access techniques should not be seen as a competition or a replacement for existing AAC methods that have a history of success. Rather, the purpose of BCI-based communication is to provide a feature-matched alternate or complementary method for accessing AAC for individuals with suitability, preference, and motivation for BCI or for those who are unable to utilize current communicative methods. Topic 6: Limitations of BCI and Future Directions Future applications of noninvasive BCIs will continue to focus on increasing accuracy and communication rate for use either as standalone AAC options or to access existing AAC devices. One major area of future work is to improve the techniques for noninvasively recording brain activity needed for BCI operation. Though a large majority of people who may potentially use BCI have reported that they are willing to wear an EEG cap (84%; Huggins, Wren, & Gruis, 2011), the application of EEG sensors and their stability over time are still obstacles needed to be overcome for practical use. Most EEG-based BCI systems require the application of electrolytic gel to bridge the contact between electrodes and the scalp for good signal acquisition. Unfortunately, this type of application has been reported to be inconvenient and cumbersome by individuals who currently use BCI and may also be difficult to set up and maintain by a trained facilitator (Blain-Moraes, Schaff, Gruis, Huggins, & Wren, 2012). Further, electrolytic gels dry out over time, gradually degrading EEG signal acquisition. Recent advances in dry electrode technology may help overcome this limitation (Blain-Moraes et al., 2012) by allowing for recording of EEG without electrolytic solutions and may lead to easier American Journal of Speech-Language Pathology • Vol. 27 • 1–12 • February 2018 application of EEG sensors and prolonged stability of EEG signal acquisition. In order to be used in all environments, EEG must be portable and robust to external sources of noise and artifacts. EEG is highly susceptible to electrical artifacts from the muscles, environment, and other medical equipment (e.g., mechanical ventilation). Therefore, an assessment is needed for likely environments of use, as are guidelines for minimizing the effect of these artifacts. Simultaneous efforts should be made toward improving the tolerance of EEG recording equipment to these outsize sources of electrical noise (Kübler et al., 2015). The ultimate potential of BCI technology is the development of a system that can directly decode brain activity into communication (e.g., written text or spoken), rather than indirectly operate a communication device. This type of neural decoding is primarily under investigation using invasive methods using electrocorticography and intracortical microelectrodes and has focused on decoding phonemes (Blakely et al., 2008; Brumberg et al., 2011; Herff et al., 2015; Mugler et al., 2014; Tankus et al., 2012), words (Kellis et al., 2010; Leuthardt et al., 2011; Pei et al., 2011), and time-frequency representations (Martin et al., 2014). Invasive methods have the advantage of increased signal quality and resistance to sources of external noise but require a surgical intervention to implant recording electrodes either in or on the brain (Chakrabarti et al., 2015). The goal of these decoding studies and other invasive electrophysiological investigations of speech processing is to develop a neural prosthesis for fluent-like speech production (Brumberg, Burnison, & Guenther, 2016). Although invasive techniques come at a surgical cost, one study reported that 72% of individuals with ALS indicated they were willing to undergo outpatient surgery, and 41% were willing to have a surgical intervention with a short hospital stay to access invasive BCI methods (Huggins et al., 2011). That said, very few invasive BCIs are available for clinical research or long-term at-home use (e.g., Vansteensel et al., 2016); therefore, noninvasive methods will likely be first adopted for use in AAC interventions. Conclusions This tutorial has focused on a few important considerations for the future of BCIs as AAC: (a) Despite broad speech-language pathology expertise in AAC, there are few clinical guidelines and recommendations for the use of BCI as an AAC access technique; (b) the most mature BCI technologies have been designed as methods to access communication interfaces rather than directly accessing thoughts, utterances, and speech motor plans from the brain; and (c) BCI is an umbrella term for a variety of brain-tocomputer techniques that require comprehensive assessment for matching people who may potentially use BCI with the most appropriate device. The purpose of this tutorial was to bridge the gaps in knowledge between AAC and BCI practices, describe BCIs in the context of current AAC conventions, and motivate interdisciplinary collaborations to pursue rigorous clinical research to adapt AAC feature matching protocols to include intervention with BCIs. A summary of take-home messages to help bridge the gap between knowledge of AAC and BCI was compiled from our interdisciplinary team and summarized in Table 3. Additional training and hands-on experience will improve acceptance of BCI approaches for interventionists targeted by this tutorial, as well as people who may use BCI in the future. Key to the clinical acceptance of BCI are necessary improvements in communication rate and accuracy via BCI access methods (Kageyama et al., 2014). However, many people who may use BCIs understand the current limitations, yet they recognize the potential positive benefits of BCI, reporting that the technology offers “freedom,” “hope,” “connection,” and unlocking from their speech and motor impairments (Blain-Moraes et al., 2012). A significant component of future BCI research will focus on meeting the priorities of people who use BCIs. A recent study assessed the opinions and priorities of individuals with ALS in regard to BCI design and reported that individuals with ALS prioritized performance accuracy of at least 90% and a rate of at least 15 to 19 letters per minute (Huggins et al., 2011). From our review, most BCI technologies have not yet reached these specifications, though some recent efforts have made considerable progress (e.g., Chen et al., 2015; Townsend & Platsko, 2016). A renewed emphasis on user-centered design and development is helping to move this technology forward by best matching the wants and needs of individuals who may use BCI with realistic expectations of BCI function. It is imperative to include clinicians, individuals who use AAC and BCI, and other stakeholders into the BCI design process to improve usability and performance and to help find the optimal translation from the laboratory to the real world. 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Building evidence-based practice in AAC display design for young children: Current 12 practices and future directions. Augmentative and Alternative Communication, 31(2), 124–136. Townsend, G., & Platsko, V. (2016). Pushing the P300-based brain–computer interface beyond 100 bpm: Extending performance guided constraints into the temporal domain. Journal of Neural Engineering, 13(2), 026024. Trnka, K., McCaw, J., Yarrington, D., McCoy, K. F., & Pennington, C. (2008). Word prediction and communication rate in AAC, In Proceedings of the IASTED International Conference on Telehealth/Assistive Technologies (Telehealth/AT ’08) (pp. 19–24). Baltimore, MD: ACTA Press Anaheim, CA, USA. Vansteensel, M. J., Pels, E. G. M., Bleichner, M. G., Branco, M. P., Denison, T., Freudenburg, Z. V., . . . Ramsey, N. F. (2016). Fully implanted brain–computer interface in a locked-in patient with ALS. New England Journal of Medicine, 375(21), 2060–2066. Vaughan, T. M., McFarland, D. J., Schalk, G., Sarnacki, W. A., Krusienski, D. J., Sellers, E. W., & Wolpaw, J. R. (2006). The Wadsworth BCI Research and Development Program: At home with BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(2), 229–233. Vidaurre, C., & Blankertz, B. (2010). Towards a cure for BCI illiteracy. Brain Topography, 23(2), 194–198. Volosyak, I., Valbuena, D., Lüth, T., Malechka, T., & Gräser, A. (2011). BCI demographics II: How many (and what kinds of ) people can use a high-frequency SSVEP BCI? IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19(3), 232–239. Vuckovic, A., & Osuagwu, B. A. (2013). Using a motor imagery questionnaire to estimate the performance of a brain–computer interface based on object oriented motor imagery. Clinical Neurophysiology, 124(8), 1586–1595. Wills, S. A., & MacKay, D. J. C. (2006). DASHER—An efficient writing system for brain–computer interfaces? IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(2), 244–246. Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002). Brain–computer interfaces for communication and control. Clinical Neurophysiology, 113(6), 767–791. Wolpaw, J. R., & McFarland, D. J. (2004). Control of a twodimensional movement signal by a noninvasive brain–computer interface in humans. Proceedings of the National Academy of Sciences of the United States of America, 101(51), 17849–17854. American Journal of Speech-Language Pathology • Vol. 27 • 1–12 • February 2018 Copyright of American Journal of Speech-Language Pathology is the property of American Speech-Language-Hearing Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. Augmentative and Alternative Communication, 2015; 31(2): 124–136 © 2015 International Society for Augmentative and Alternative Communication ISSN 0743-4618 print/ISSN 1477-3848 online DOI: 10.3109/07434618.2015.1035798 RESEARCH ARTICLE Building Evidence-based Practice in AAC Display Design for Young Children: Current Practices and Future Directions JENNIFER J. THISTLE & KRISTA M. WILKINSON Department of Communication Sciences and Disorders, The Pennsylvania State University, University Park, PA, USA Abstract Each time a practitioner creates or modifies an augmentative and alternative communication (AAC) display for a client, that practitioner must make a series of decisions about which vocabulary concepts to include, as well as physical and organizational features of the display. Yet, little is known about what factors influence the actual decisions and their outcomes. This research examined the design factors identified as priorities by speech-language pathologists (SLPs) when creating AAC displays for young children (age 10 years and under), and their rationale for the selection of these priorities. An online survey gathered ratings and comments from 112 SLPs with experience in AAC concerning the importance of a variety of factors related to designing an aided AAC display. Results indicated that some decisions were supported by existing research evidence, such as choosing vocabulary, collaborating with key stakeholders, and supporting partner modeling. Other decisions highlight areas for future research, including use of visual scene display layouts, symbol background color, and supports for motor planning. Keywords: Clinical practices; Display design; Survey; Augmentative and alternative communication Introduction of an individual, and using these characteristics of the individual to drive the selection and design of the system (Light & McNaughton, 2013a). For example, depending on the individual’s visual or motor access abilities, the size of the symbols may or may not be an important feature to manipulate (Kovach & Kenyon, 2003). A 2006 survey examined SLPs’ perceptions on what contributes to success and abandonment of AAC technology (Johnson, Inglebret, Jones, & Ray, 2006). SLPs reported that an appropriate match between the individual and the system is one factor that promotes greater success with the device. Intrinsic abilities such as motor, cognitive/linguistic, literacy skills, and sensory perceptual skills must be assessed and compared to external features of systems to determine the best match. With the great variety of aided AAC technologies available, matching external features to intrinsic abilities is no small task. Ideally, practitioners are combining their practical knowledge and experiences with available evidence to inform a trial-based, feature-matching approach. However, such an approach may increase the time it takes an individual to reach competence with a system. Rather, if there are design decisions that follow specific patterns, these could potentially reduce the number of trials needed to identify the best fit for Individuals with complex communication needs often rely on augmentative and alternative communication (AAC) to participate in communication interactions. An AAC system encompasses a variety of methods to support communication, such as gestures, sign language, communication boards, and speech generating devices (Beukelman & Mirenda, 2013). Techniques that utilize tools outside of the body, such as a communication board with graphic symbols or a computer programmed with voice output, are called aided AAC. Substantial evidence suggests the use of AAC interventions increases language development with individuals with a variety of communication disabilities (e.g., Binger & Light, 2007; Drager et al., 2006; Romski & Sevcik, 1996). Once a system is selected, AAC intervention requires more than taking the device out of the box and handing it over to the individual. One of the challenges facing practitioners such as speech-language pathologists (SLPs), special education teachers, and occupational therapists is creating an aided AAC system that maintains an appropriate balance between the benefits of the communication afforded by the system, and the costs of learning how to use it (Beukelman, 1991). Achieving this balance requires determining the needs and abilities Correspondence: Jennifer Thistle, Department of Communication Sciences and Disorders, HSS 112, University of Wisconsin-Eau Claire, Eau Claire, WI 54702, USA. Tel: ⫹ 1 715 836 6015. E-mail: (Received 28 March 2014; revised 23 March 2015; accepted 24 March 2015) 124 AAC Display Design Decisions the individual. The first step in identifying decision processes that are more likely to result in positive outcomes is to better understand the factors considered by practitioners in designing AAC displays. There has been limited research specifically exploring the kinds of decisions SLPs and other practitioners make related to display design. One of the only studies to date that examined this topic was a preliminary and qualitative study using a hypothetical case study format. Specifically, McFadd and Wilkinson (2010) provided six clinicians who specialize in the field of AAC with a partial list of vocabulary to include on an aided AAC display. Clinicians selected additional vocabulary and created a low-tech communication board for a hypothetical student to use during snack time. Clinicians were asked to narrate their thought processes while making decisions regarding the selection and arrangement of vocabulary on the display. The clinicians in McFadd and Wilkinson (2010) applied research-based recommendations by incorporating vocabulary that supported a variety of communicative functions (Adamson, Romski, Deffebach, & Sevcik, 1992; Light & Drager, 2005). For example, five of the clinicians included verbs and social terms to support a variety of interactions. Another clinician only included object labels, but described her rationale that those labels would support social communication by allowing the child and peers to talk about the foods they were eating. Five of the six clinicians used Boardmaker Picture Communication Symbols (PCS; Mayer-Johnson, 1992) to represent the content, based on the instruction from the researchers; the sixth pulled similar types of images from the Internet because her school did not have access to the Boardmaker program. Choices related to arrangement of the symbols were less consistent across clinicians. All clinicians organized the vocabulary in some fashion, and those that included different types of words (e.g., object, verbs, socialregulatory) created subsets based on those types. However, the placement of the subsets varied. For instance, some clinicians placed social-regulatory symbols along the top row while others placed the same symbols along the left-hand column. Still another clinician used spacing within the page to separate types of words. Finally, some clinicians used background color to distinguish different symbol word classes while another placed the symbols on a colored page to support navigation across multiple pages. One challenge when examining clinical practices lies in the heterogeneity of individuals who can benefit from AAC systems. Children with developmental disorders, for example, may have very different needs and abilities compared to adults with acquired disorders. The current study focused on practitioners, specifically SLPs, working with young children (aged 10 years and under) in an attempt to constrain some of the variability seen in AAC decision-making. Even when limited to elementary school children, however, the caseloads of SLPs will influence the experiences upon which they draw when © 2015 International Society for Augmentative and Alternative Communication 125 designing AAC displays. In the 2014 American SpeechLanguage-Hearing Association (ASHA) Schools Survey (ASHA, 2014), the 55% of SLPs who regularly provided AAC-related services reported serving an average of five students. This represents 10% of an elementary school SLPs’ average monthly caseload, and 20% of a residential/special day school SLPs’ average monthly caseload (ASHA, 2014). Furthermore SLPs who work in residential/special day schools reported that 71% of their caseloads consisted of students with severe communication impairments. It is likely, then, that SLPs will have had different experiences designing AAC displays. Professional preparation in AAC also may influence SLPs’ comfort level with providing AAC services, thereby affecting the decisions they make when designing AAC displays. In a survey of 71 SLPs, 72% rated their competence in providing AAC services as fair to poor (Marvin, Montano, Fusco, & Gould, 2003). Similar results emerged in surveys conducted in Egypt (Wormnaes & Abdel Malek, 2004) and New Zealand (Sutherland, Gillon, & Yoder, 2005). Such low levels of competence may be precipitated by education and training provided by SLP programs. In a survey of SLP training programs, 33% of respondents felt that the majority of their students were prepared to work with individuals who use AAC (Ratcliff, Koul, & Lloyd, 2008). Just under half (47%) of the respondents reported that only up to one quarter of their students receive clinical practice in AAC. In a review of research conducted from 1985–2009, Costigan and Light (2010) examined surveys of pre-service AAC training programs in the US, and reported that the majority of SLPs received minimal to no pre-service training in AAC. Thus, it is possible that the variability noted between SLPs in McFadd and Wilkinson’s (2010) study reflected the SLPs’ educational background and experiences with individualizing displays for the wide variety of children who use AAC. If there are some practices that professionals have found to be more successful than other practices, it is important to identify the successful approaches in order to reduce the number of trials of different features that an individual who uses AAC must go through. Research Questions This research addressed the following question: What design factors do SLPs identify as priorities when they create aided AAC displays for young school-aged children, and what are their rationales for the selection of these factors as priorities? Through an online survey, participants answered questions related to the decisions they make regarding vocabulary selection, symbol types and arrangement, and manipulation of a variety of visual features (e.g., size, color, etc.) of aided AAC displays. The responses were analyzed not only to gain a broad view of the general clinical practices but also to understand the factors that might influence the decision-making process. 126 J. J. Thistle & K. M.Wilkinson Method Survey Development Survey questions were developed and refined through initial pilot testing and reviews by experts in survey design. Initial questions were developed to target decisions related to vocabulary selection, symbol types and arrangement, and manipulation of a variety of visual features of aided AAC displays. The initial pilot survey from which the final version was developed was completed by three SLPs, with an average of 8 years (range: 7–10 years) experience providing AAC services to children. Feedback from the pilot participants ensured that the focus of the questions centered on the goals of the study. The university’s Survey Research Center then reviewed the survey for structure and adherence to survey design principles. As a result, demographic questions were moved from the beginning to the end of the survey, based on the rationale that it may be perceived as less intrusive to answer personal demographic questions at the end of the survey (Groves et al., 2009). The final version of the survey consisted of 42 questions intended to solicit information about two aspects of display design: the principles guiding aided AAC display design in general, and child-specific decisions driven by a given case study. Participants advanced through the survey sequentially (answering the general questions first, then the child-specific questions) and received an error message if they attempted to advance without completing a question. Therefore, as the survey progressed, no questions could be skipped. Because the survey could be abandoned prior to completion, a greater number of responses were provided to questions asked earlier in the survey than those that were asked later. The current study reports on responses related to the first section because the goal was to outline general principles guiding aided AAC display design decisions. Appendix A (to be found online at presents these survey questions. The results of the answers related to the specific case will be reported in a separate study. Participants Target participants were practicing SLPs who (a) maintained a current certificate of clinical competence from ASHA, (b) had at least 1 year of experience supporting individuals who use AAC, and (c) provided AAC services to school-age children aged 10 and under. The online survey was available for 12 weeks and participants were recruited through multiple contact points to allow for adequate opportunity for responding and to increase sample size (Dillman, Smyth, & Christian, 2009). Qualtrics1 online survey software hosted the web-based survey. Participants completed the survey at a computer of their choosing and were able to take the survey over multiple sessions if they chose to do so. The University’s Office for Research Protections provided human subjects approval for this research project. An implied consent form was embedded as the first page of the online survey and participants were advised that continuing the survey indicated consent. Participants had the option of downloading the implied consent form if desired. Survey Distribution Members of two list serves were contacted at three time points. The list serves were the ASHA Special Interest Group 12-Augmentative and Alternative Communication (SIG-12) and Quality Indicators for Assistive Technology (QIAT). A general recruitment notice describing the study and soliciting participation was posted to each list serve at the initial time point, 3 weeks later, and again 7 weeks from the initial posting. Throughout this data collection period, in-person recruitment also occurred during the ISAAC biennial convention. Finally, appeals to personal contacts and postings on social media websites provided additional advertising regarding availability of the survey. Data Analysis The survey consisted of a mix of open-ended and closed-ended questions. Descriptive methods of data analysis were utilized due to the exploratory nature of the questions and the goal of the survey to identify trends to inform future research directions. Descriptive data in the form of frequency tables were used to examine the closed-ended questions. The open-ended questions were coded for common themes using scrutiny techniques (Ryan & Bernard, 2003). Like Ryan and Bernard, three research assistants and the first author initially identified themes and subthemes by reading each response and listing commonly repeated terms and identifying similarities and differences in responses. Refinement of the themes and subthemes occurred during a cycle of consensus coding. The research team formally defined the codes in a codebook that contained the definition as well as examples and non-examples. A summary page of the codes is presented in Appendix B (to be found online at 07434618.2015.1035798). Two of the primary codes were each further refined into five secondary codes, resulting in 16 possible codes. The primary codes were used to identify responses that (a) were unclear to the coder, (b) noted features the participant did not think were important, (c) related to the child’s skills and abilities, (d) related to the communication demands, (e) related to the AAC device, and (f) related to key stakeholders (e.g., clinicians, teachers, communication partners). The secondary codes provided detail related to the child’s abilities (e.g., vision abilities) and the communication demands (e.g., functional vocabulary for the setting). The first author divided the responses into individual thought units consisting of the smallest meaningful piece Augmentative and Alternative Communication AAC Display Design Decisions of information contained in the response (Fraenkel, 2006). Typically, the thought units corresponded with a participant’s sentence. However, when the participant included a variety of ideas in one sentence, the resulting thought units were individual words or phrases. Research assistants then assigned one code per thought unit. Inter-observer agreement was assessed on the final coding of the thought units. Two research assistants each independently coded thought units. After a period of training, the coders reached a minimum Kappa coefficient of .7 (Fleiss, Levin, & Paik, 2003). Coders then individually coded all questions and subsequent reliability was recalculated on 25% of the responses. Agreement on each question was on average .76 (range .71–.78). Kappa values of .4–.75 are considered good and values of .75 or greater signify excellent agreement (Fleiss et al., 2003). Results Responses In total, 192 individuals accessed the survey. Of those, 24 dropped out during the initial screening section, 17 were excluded because they did not meet the selection criteria, and two reported living outside the United States, in Canada and South Africa. Due to the small number of international participants, these responses were excluded from the final analysis. Of the 149 eligible participants, 112 completed the broad design questions2 but provided only some demographic data, 77 completed the entire survey (including all demographic data), and 37 did not complete the primary questions. The presentation of the results follows the sequence of the survey, although demographics and initial design decisions for the 77 participants who provided that information will be described first. A discussion of the clinical implications and future directions follow the survey results. Demographics Of the 77 participants who provided complete demographic data, 60 (78%) were members of ASHA SIG-12, and nearly half (48%) reported living in the Northeast. Table I presents a summary of the demographic information, including distribution by geographical region, participant gender, and race/ethnicity. One of the screening questions asked participants’ years of experience supporting children who use AAC. Thus, although only 77 participants completed the demographics section of the survey, all 112 participants provided years of experience. Table II presents the proportion of participants who completed the broad design questions but did not complete the demographic questions and those who completed both sections by their level of experience. The following results address similarities and differences observed in responses across the different levels of experience. © 2015 International Society for Augmentative and Alternative Communication 127 Table I. Percentage of Participants by Geographical Region, Gender, and Race/Ethnicity. Characteristic Participants (%) Geographic region Northeast Southeast North Central South Central West/Mountain Gender Female Male Race/ethnicity White/Caucasian African American Hispanic Asian n % 37 12 7 6 15 48.0 7.8 15.6 9.1 19.5 75 2 97.4 2.6 72 1 3 1 93.5 1.3 3.9 1.3 Initial Design Decisions One of the first decisions a clinician must make when creating a new display for young children is whether to modify the page set provided by the manufacturer. Of the 77 participants answering this question, 60 (78%) reported often or always making changes to the page set provided by the AAC manufacturer, and 8 (10%) reported rarely or never making changes to the page set provided by the manufacturer. An examination of the responses by level of experience did not reveal a distinctive pattern related to experience level. Across most experience levels, only 9% (5 out of 53) of the participants reported rarely or never making changes. However, 21% (3 out of 14) of the participants with 13–20 years of experience reported rarely or never making changes. This difference in responding by participants with this level of experience recurs throughout the survey and will be explored in the Discussion section. Decisions Related to Vocabulary Selection SLPs often play a key role in choosing the display content, including what concepts and communication functions the content supports. Several themes emerged in terms of decisions made by SLPs with regard to the importance of child preferences, other stakeholders, the role of core vocabulary, and the range of word classes to include. Table II. Number and Percentage of Participants Completing the Broad Design and the Demographic Questions Sections. Years of experience 1–3 4–7 8–12 13–20 21 or more Completed demographic questions (n ⫽ 77) n % 8 23 13 14 19 10.4 29.9 16.9 18.2 24.6 Completed only broad design questions (n ⫽ 35) n % 3 7 5 11 9 8.6 20.0 14.3 31.4 25.7 128 J. J. Thistle & K. M.Wilkinson Child Preferences. The child’s preferences were noted to be extremely or fairly important in vocabulary selection by 87% (97 out of 112) of respondents. Figure 1 illustrates the level of importance participants placed upon the child’s preferences in their vocabulary selection process, for each level of clinician experience. All of the participants with 1–3 years of experience felt the child’s preferences were extremely or fairly important. On the other hand, 28% (7 of 25) of respondents with 13–20 years of experience indicated that the child’s preferences were only somewhat or not very important. This accounted for half of the 13% (15 out of 112) of all participants who felt that child’s preferences were somewhat or not very important. Additional Priorities. An open-ended question provided some insight into what additional priorities respondents felt were important when choosing vocabulary. Figure 2 illustrates the main categories of priorities identified by participants by years of experience. Key Stakeholders. As a whole, 46 of 112 (41%) participants mentioned collaborating with key stakeholders. Once again, however, there was a somewhat unusual pattern in the group with 13–20 years’ experience, as only 20% (5 out of 25) mentioned key stakeholders compared to 41% (41) of the remaining 87 participants. Some participants mentioned specific instances that would influence vocabulary selection. One wrote, “I add vocabulary based on family preferences too – parents often like some of the politeness terms, for instance.” Other participants were more general in their description and rationale of including key stakeholders: “I take into account what the family and classroom find important.” Role of Core Vocabulary. Clinicians with more than 13 years of experience reported choosing core vocabulary based on frequency of words more often (15%, 8 out of 53) than those with less than 13 years of experience (8%, 5 out of 59). Some respondents prioritized core vocabulary above the child’s or key stakeholders’ preferences. For instance, one participant stated, “I would ONLY consider the child’s vocabulary preferences Figure 2. Percentage of participants mentioning additional vocabulary selection considerations beyond a child’s preferences, by years of experience. when I am including personal words that are part of the child’s extended vocabulary set. These personal extended vocabulary words are second on my list after CORE vocabulary” (emphasis provided by participant). This quote also illustrates the challenge inherent in design decision – many decisions influence other decisions, and trade-offs must be made. In this case, it seems the participant was making a choice between providing core vocabulary or personalized vocabulary. Range of Word Classes. Most participants indicated that they frequently used a variety of word forms in support of language acquisition and use. Specifically, participants incorporated subjects (82%, 92 of 112), actions (97%, 109 of 112), objects (83%, 93 of 112), and emotion words (84%, 94 of 112) most or all of the time. Figure 3 shows the frequency with which participants incorporate each of these types of words by their level of experience. In all, 93% (26 out of 28) of participants with the most experience reported incorporating emotion words most or all of the time, whereas on average 81% (68 out of 84) of the clinicians with other levels of experience incorporated emotion words most or all of the time. Decisions Related to Symbol Type Following identification of appropriate content, SLPs consider options regarding how best to represent that content. The type of symbol representation was rated as fairly or extremely important by 100% of participants with 1–3 years’ experience (n ⫽ 11), but only 76% (77 out of 101) of all other participants. When asked what factors influence their choice of symbol type, 90% (10 out of 11) of the participants with 1–3 years of experiences cited the child’s cognitive abilities as an important consideration. Across all other experience levels, just under half (45%, 45 out of 101) reported that the child’s cognitive level should be considered. Decisions Related to Visual Features of the Display Figure 1. Percentage of participants who rated child’s preference in vocabulary selection as either fairly/extremely important or not very/ somewhat important, by years of experience. The visual nature of an aided AAC display allows for manipulation of such features as symbol arrangement Augmentative and Alternative Communication AAC Display Design Decisions 129 Figure 3. Percent of participants indicating the frequency with which they include subjects, action words, descriptors, and emotion words in displays, by years of experience. and display layout, symbol size, and use of color. Three themes that emerged from this survey concerned choices related to (a) the type of display layout, (b) the use of black and white versus colored symbols, and (c) the use of background color cuing. Type of Display Layout. When asked to estimate the percentage of hybrid or visual scene displays (VSDs) participants design, 83% (64 out of 77) reported using these displays less than 25% of the time, suggesting grid-based displays were used most of the time. Only 3% (2 out of 77) of participants reported using VSD/hybrid displays more than 50% of the time. Use of Symbol Internal Color. Of the 112 participants, 94 (84%) reported utilizing symbols that have internal color most or all of the time; only one participant indicated very rarely using color symbols, but did not provide a reason in the following open-ended question. Despite the widespread use of symbol color, 78 (69%) reported using black and white symbols some of the time. Participants reported that black and white symbols were used to highlight new symbols, when color would not contribute to the meaning (e.g., prepositional words), or when team members did not have access to color printers. Use of Background Color. Color can also be featured in the background of symbols. All participants reported using symbol background color at least sometimes, and 49 (43%) reported that they used it most of the time, a trend that was consistent across all experience levels. The top two reasons provided regarding use of background color were (a) to support development of grammatical skills through color coding parts of speech, and © 2015 International Society for Augmentative and Alternative Communication (b) to draw attention to specific symbols. Using background color as a cue to word class category reflects a common clinical recommendation (Goossens’, Crain, & Elder, 1999; Kent-Walsh & Binger, 2009); however, to date there has been no research that specifically examines if the dimension of color aids in learning appropriate sentence structure. Additional Decisions When given the opportunity to discuss any additional general factors not previously mentioned, 32% (36 out of 112) of participants supported the use of consistency in display design to support motor planning and automaticity. In this approach, the location of previously learned symbols on the display does not change as new symbols are added to the display. Finally, an additional feature participants consider was mentioned in response to several different, unrelated, open-ended questions: designing the display in a way that supports partner modeling. Discussion The goal of AAC intervention for a child is to provide support for participation and language development across all environments, facilitating early communication (Light & Drager, 2005; Romski & Sevcik, 1996), advancing linguistic growth and functional communication (Binger & Light, 2007; Drager et al., 2006; Johnston, McDonnell, Nelson, & Magnavito, 2003), and providing early literacy experiences (Koppenhaver & Erickson, 2003; Light & McNaughton, 2013b). Designing an appropriate AAC display is one part of AAC intervention that may contribute to this goal. There are 130 J. J. Thistle & K. M.Wilkinson many factors to consider when designing an AAC display, including but not limited to appeal of the display (Light, Drager, & Nemser, 2004; Light, Page, Curran, & Pitkin, 2007), ease of use (Drager, Light, Speltz, Fallon & Jeffries, 2003; Drager et al., 2004; Fallon, Light, & Achenbach, 2003), and communicative functions supported by the display (Adamson et al., 1992; Romski & Sevcik, 1996). Furthermore, these considerations must be weighed against the needs and abilities of the child who will be using the display (Light & McNaughton, 2013a). Certainly, as a field, we are at times successful as we strive toward this goal; at other times, however, we do not succeed (Johnson et al., 2006; Snell et al., 2010). In this study, SLPs reported considering a number of factors in the development of AAC displays, suggesting at least some awareness of the need for AAC systems to be responsive to the needs of the child and team. This is in line with the multifaceted nature of AAC interventio...
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