Kevin M Pitt1, Jonathan S Brumberg2, Jeremy D Burnison3, Jyutika Mehta4, Juhi Kidwai2. 1. Department of Special Education and Communication Disorders, University of Nebraska-Lincoln, Lincoln, NE. 2. Department of Speech-Language-Hearing: Sciences & Disorders, University of Kansas, Lawrence, KS. 3. Department of Scientific Support, Brain Vision LLC, Morrisville, NC. 4. Department of Communication Sciences & Disorders, Texas Woman's University, Denton, TX.
Abstract
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 1) how BCI signals are recorded via EEG, 2) what signals are targeted for non-invasive BCI control, including the P300, sensorimotor rhythms, steady state evoked potentials, contingent negative variation, and the N400, and 3) 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.
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 1) how BCI signals are recorded via EEG, 2) what signals are targeted for non-invasive BCI control, including the P300, sensorimotor rhythms, steady state evoked potentials, contingent negative variation, and the N400, and 3) 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.
Entities:
Keywords:
AAC; BCI; Brain-computer interface; EEG; N400; P300; augmentative and alternative communication; contingent negative variation; electroencephalography; event-related potential; sensorimotor rhythm; steady state evoked potentials
Authors: N Jeremy Hill; Erin Ricci; Sameah Haider; Lynn M McCane; Susan Heckman; Jonathan R Wolpaw; Theresa M Vaughan Journal: J Neural Eng Date: 2014-05-19 Impact factor: 5.379
Authors: Adrien Combaz; Camille Chatelle; Arne Robben; Gertie Vanhoof; Ann Goeleven; Vincent Thijs; Marc M Van Hulle; Steven Laureys Journal: PLoS One Date: 2013-09-25 Impact factor: 3.240