Brain–Computer Interfaces for Augmentative and Alternative Communication: Separating the Reality From the Hype

2018 ◽  
Vol 3 (12) ◽  
pp. 13-23 ◽  
Author(s):  
Jane E. Huggins ◽  
Thomas Kovacs
Keyword(s):  
Augmentative And Alternative Communication ◽  
Brain Computer Interfaces ◽  
Alternative Communication ◽  
Computer Interfaces

scholarly journals Noninvasive Brain–Computer Interfaces for Augmentative and Alternative Communication

2014 ◽  
Vol 7 ◽  
pp. 31-49 ◽  
Author(s):  
Murat Akcakaya ◽  
Betts Peters ◽  
Mohammad Moghadamfalahi ◽  
Aimee R. Mooney ◽  
Umut Orhan ◽  
...  
Keyword(s):  
Augmentative And Alternative Communication ◽  
Brain Computer Interfaces ◽  
Alternative Communication ◽  
Computer Interfaces

scholarly journals Brain–Computer Interfaces for Augmentative and Alternative Communication: A Tutorial

2018 ◽  
Vol 27 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Jonathan S. Brumberg ◽  
Kevin M. Pitt ◽  
Alana Mantie-Kozlowski ◽  
Jeremy D. Burnison
Keyword(s):  
Clinical Application ◽  
Augmentative And Alternative Communication ◽  
Scope Of Practice ◽  
Brain Computer Interfaces ◽  
Alternative Communication ◽  
Access Methods ◽  
Computer Interfaces ◽  
Current State ◽  
Starting Point ◽  
Areas Of Interest

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.

scholarly journals Guidelines for Feature Matching Assessment of Brain–Computer Interfaces for Augmentative and Alternative Communication

2018 ◽  
Vol 27 (3) ◽  
pp. 950-964 ◽  
Author(s):  
Kevin M. Pitt ◽  
Jonathan S. Brumberg
Keyword(s):  
Motor Imagery ◽  
Augmentative And Alternative Communication ◽  
Feature Matching ◽  
Full Range ◽  
Brain Computer Interfaces ◽  
Alternative Communication ◽  
Access Methods ◽  
Computer Interfaces ◽  
Sensory Motor ◽  
Selection Of

Purpose Brain–computer interfaces (BCIs) can provide access to augmentative and alternative communication (AAC) devices using neurological activity alone without voluntary movements. As with traditional AAC access methods, BCI performance may be influenced by the cognitive–sensory–motor and motor imagery profiles of those who use these devices. Therefore, we propose a person-centered, feature matching framework consistent with clinical AAC best practices to ensure selection of the most appropriate BCI technology to meet individuals' communication needs. Method The proposed feature matching procedure is based on the current state of the art in BCI technology and published reports on cognitive, sensory, motor, and motor imagery factors important for successful operation of BCI devices. Results Considerations for successful selection of BCI for accessing AAC are summarized based on interpretation from a multidisciplinary team with experience in AAC, BCI, neuromotor disorders, and cognitive assessment. The set of features that support each BCI option are discussed in a hypothetical case format to model possible transition of BCI research from the laboratory into clinical AAC applications. Conclusions This procedure is an initial step toward consideration of feature matching assessment for the full range of BCI devices. Future investigations are needed to fully examine how person-centered factors influence BCI performance across devices.

scholarly journals Behind the Scenes of Noninvasive Brain–Computer Interfaces: A Review of Electroencephalography Signals, How They Are Recorded, and Why They Matter

2019 ◽  
Vol 4 (6) ◽  
pp. 1622-1636
Author(s):  
Kevin M. Pitt ◽  
Jonathan S. Brumberg ◽  
Jeremy D. Burnison ◽  
Jyutika Mehta ◽  
Juhi Kidwai
Keyword(s):  
Augmentative And Alternative Communication ◽  
Computer Interface ◽  
Computer Access ◽  
Alternative Communication ◽  
Primary Interest ◽  
Physical Impairments ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Speech Science ◽  
Sensorimotor Rhythms

Purpose Brain–computer interface (BCI) techniques may provide computer access for individuals with severe physical impairments. However, the relatively hidden nature of BCI control obscures how BCI systems work behind the scenes, making it difficult to understand “how” electroencephalography (EEG) records the BCI-related brain signals, “what” brain signals are recorded by EEG, and “why” these signals are targeted for BCI control. Furthermore, in the field of speech-language-hearing, signals targeted for BCI application have been of primary interest to clinicians and researchers in the area of augmentative and alternative communication (AAC). However, signals utilized for BCI control reflect sensory, cognitive, and motor processes, which are of interest to a range of related disciplines, including speech science. Method This tutorial was developed by a multidisciplinary team emphasizing primary and secondary BCI-AAC–related signals of interest to speech-language-hearing. Results An overview of BCI-AAC–related signals are provided discussing (a) “how” BCI signals are recorded via EEG; (b) “what” signals are targeted for noninvasive BCI control, including the P300, sensorimotor rhythms, steady-state evoked potentials, contingent negative variation, and the N400; and (c) “why” these signals are targeted. During tutorial creation, attention was given to help support EEG and BCI understanding for those without an engineering background. Conclusion Tutorials highlighting how BCI-AAC signals are elicited and recorded can help increase interest and familiarity with EEG and BCI techniques and provide a framework for understanding key principles behind BCI-AAC design and implementation.

scholarly journals Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Individuals with a Speech Disability

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1911 ◽  
Author(s):  
Yasmin Elsahar ◽  
Sijung Hu ◽  
Kaddour Bouazza-Marouf ◽  
David Kerr ◽  
Annysa Mansor
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Augmentative And Alternative Communication ◽  
High Tech ◽  
Alternative Communication ◽  
Computer Interfaces ◽  
Ease Of Access

High-tech augmentative and alternative communication (AAC) methods are on a constant rise; however, the interaction between the user and the assistive technology is still challenged for an optimal user experience centered around the desired activity. This review presents a range of signal sensing and acquisition methods utilized in conjunction with the existing high-tech AAC platforms for individuals with a speech disability, including imaging methods, touch-enabled systems, mechanical and electro-mechanical access, breath-activated methods, and brain–computer interfaces (BCI). The listed AAC sensing modalities are compared in terms of ease of access, affordability, complexity, portability, and typical conversational speeds. A revelation of the associated AAC signal processing, encoding, and retrieval highlights the roles of machine learning (ML) and deep learning (DL) in the development of intelligent AAC solutions. The demands and the affordability of most systems hinder the scale of usage of high-tech AAC. Further research is indeed needed for the development of intelligent AAC applications reducing the associated costs and enhancing the portability of the solutions for a real user’s environment. The consolidation of natural language processing with current solutions also needs to be further explored for the amelioration of the conversational speeds. The recommendations for prospective advances in coming high-tech AAC are addressed in terms of developments to support mobile health communicative applications.

scholarly journals Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Speech Disabled Individuals

2019 ◽  
Author(s):  
Yasmin Elsahar ◽  
Sijung Hu ◽  
Kaddour Bouazza-Marouf ◽  
David Kerr ◽  
Annysa Mansor
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Augmentative And Alternative Communication ◽  
High Tech ◽  
Alternative Communication ◽  
Computer Interfaces ◽  
Ease Of Access

High-tech augmentative and alternative communication (AAC) methods are on a constant rise; however, the interaction between the user and the assistive technology is still challenged for an optimal user experience centered around the desired activity. This review presents a range of signal sensing and acquisition methods utilized in conjunction with the existing high-tech AAC platforms for speech disabled individuals, including imaging methods, touch-enabled systems, mechanical and electro-mechanical access, breath-activated methods, and brain computer interfaces (BCI). The listed AAC sensing modalities are compared in terms of ease of access, affordability, complexity, portability, and typical conversational speeds. A revelation of the associated AAC signal processing, encoding, and retrieval highlights the roles of machine learning (ML) and deep learning (DL) in the development of intelligent AAC solutions. The demands and the affordability of most systems were found to hinder the scale of usage of high-tech AAC. Further research is indeed needed for the development of intelligent AAC applications reducing the associated costs and enhancing the portability of the solutions for a real user’s environment. The consolidation of natural language processing with current solutions also needs to be further explored for the amelioration of the conversational speeds. The recommendations for prospective advances in coming high-tech AAC are addressed in terms of developments to support mobile health communicative applications.

The Effect of Symbol Background Color on the Speed of Locating Targets by Adults Without Disabilities: Implications for Augmentative and Alternative Communication Display Design

2019 ◽  
Vol 4 (6) ◽  
pp. 1482-1488
Author(s):  
Jennifer J. Thistle
Keyword(s):  
Augmentative And Alternative Communication ◽  
Reaction Times ◽  
The Other ◽  
Background Color ◽  
Alternative Communication ◽  
Display Design ◽  
Line Drawings ◽  
Research With Children ◽  
Study Participants ◽  
Perceptual Factors

Purpose Previous research with children with and without disabilities has demonstrated that visual–perceptual factors can influence the speech of locating a target on an array. Adults without disabilities often facilitate the learning and use of a child's augmentative and alternative communication system. The current research examined how the presence of symbol background color influenced the speed with which adults without disabilities located target line drawings in 2 studies. Method Both studies used a between-subjects design. In the 1st study, 30 adults (ages 18–29 years) located targets in a 16-symbol array. In the 2nd study, 30 adults (ages 18–34 years) located targets in a 60-symbol array. There were 3 conditions in each study: symbol background color, symbol background white with a black border, and symbol background white with a color border. Results In the 1st study, reaction times across groups were not significantly different. In the 2nd study, participants in the symbol background color condition were significantly faster than participants in the other conditions, and participants in the symbol background white with black border were significantly slower than participants in the other conditions. Conclusion Communication partners may benefit from the presence of background color, especially when supporting children using displays with many symbols.

Improving Patient Safety and Patient–Provider Communication

2019 ◽  
Vol 4 (5) ◽  
pp. 1017-1027 ◽  
Author(s):  
Richard R. Hurtig ◽  
Rebecca M. Alper ◽  
Karen N. T. Bryant ◽  
Krista R. Davidson ◽  
Chelsea Bilskemper
Keyword(s):  
Acute Care ◽  
Augmentative And Alternative Communication ◽  
Health Care Team ◽  
Provider Communication ◽  
Alternative Communication ◽  
Patient Provider Communication ◽  
Acute Care Settings ◽  
Nurses Perceptions ◽  
Communication Needs ◽  
The Impact

Purpose Many hospitalized patients experience barriers to effective patient–provider communication that can negatively impact their care. These barriers include difficulty physically accessing the nurse call system, communicating about pain and other needs, or both. For many patients, these barriers are a result of their admitting condition and not of an underlying chronic disability. Speech-language pathologists have begun to address patients' short-term communication needs with an array of augmentative and alternative communication (AAC) strategies. Method This study used a between-groups experimental design to evaluate the impact of providing patients with AAC systems so that they could summon help and communicate with their nurses. The study examined patients' and nurses' perceptions of the patients' ability to summon help and effectively communicate with caregivers. Results Patients who could summon their nurses and effectively communicate—with or without AAC—had significantly more favorable perceptions than those who could not. Conclusions This study suggests that AAC can be successfully used in acute care settings to help patients overcome access and communication barriers. Working with other members of the health care team is essential to building a “culture of communication” in acute care settings. Supplemental Material https://doi.org/10.23641/asha.9990962

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