Surface electromyographic control of a novel phonemic interface for speech synthesis

Abstract Many individuals with minimal movement capabilities use AAC to communicate. These individuals require both an interface with which to construct a message (e.g., a grid of letters) and an input modality with which to select targets. This study evaluated the interaction of two such systems: (a) an input modality using surface electromyography (sEMG) of spared facial musculature, and (b) an onscreen interface from which users select phonemic targets. These systems were evaluated in two experiments: (a) participants without motor impairments used the systems during a series of eight training sessions, and (b) one individual who uses AAC used the systems for two sessions. Both the phonemic interface and the electromyographic cursor show promise for future AAC applications.

[1]  C. Stepp,et al.  Categorical Vowel Perception Enhances the Effectiveness and Generalization of Auditory Feedback in Human-Machine-Interfaces , 2013, PloS one.

[2]  D. Beukelman,et al.  Augmentative & Alternative Communication: Supporting Children & Adults With Complex Communication Needs , 2006 .

[3]  I. Scott MacKenzie,et al.  Effects of feedback and dwell time on eye typing speed and accuracy , 2006, Universal Access in the Information Society.

[4]  Frank H. Guenther,et al.  Brain-machine interfaces for real-time speech synthesis , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  A Kübler,et al.  A P 300-based brain-computer interface for people with amyotrophic lateral sclerosis , 2010 .

[6]  T. N. Lal,et al.  Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[7]  E. Alant,et al.  Comparison of the learnability and retention between Blissymbols and CyberGlyphs. , 2005, International journal of language & communication disorders.

[8]  Per Ola Kristensson,et al.  iSCAN: a phoneme-based predictive communication aid for nonspeaking individuals , 2012, ASSETS '12.

[9]  Gerwin Schalk,et al.  Rapid Communication with a “P300” Matrix Speller Using Electrocorticographic Signals (ECoG) , 2010, Front. Neurosci..

[10]  Cheng Zhang,et al.  An Eye-Gaze Tracking and Human Computer Interface System for People with ALS and other Locked-in Diseases , 2012 .

[11]  J. Wolpaw,et al.  A P300-based brain–computer interface for people with amyotrophic lateral sclerosis , 2008, Clinical Neurophysiology.

[12]  J. Basmajian Electromyography comes of age. , 1972, Science.

[13]  Martine Smith,et al.  Simply a Speech Impairment? Literacy Challenges for Individuals with Severe Congenital Speech Impairments , 2001 .

[14]  Susan Fager,et al.  AAC for adults with acquired neurological conditions: A review , 2007, Augmentative and alternative communication.

[15]  Cara E. Stepp,et al.  Surface electromyographic control of speech synthesis , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  Kevin Caves,et al.  Access to AAC: Present, past, and future , 2007, Augmentative and alternative communication.

[17]  R.F. Kirsch,et al.  Evaluation of Head Orientation and Neck Muscle EMG Signals as Command Inputs to a Human–Computer Interface for Individuals With High Tetraplegia , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[18]  Diane Nelson Bryen,et al.  Vocabulary to Support Socially-Valued Adult Roles , 2008, Augmentative and alternative communication.

[19]  K. Preston White,et al.  Eye-gaze word processing , 1990, IEEE Trans. Syst. Man Cybern..

[20]  Susan Fager,et al.  Access to augmentative and alternative communication: new technologies and clinical decision-making. , 2012, Journal of pediatric rehabilitation medicine.

[21]  Michael J. Black,et al.  Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array , 2011 .

[22]  J. Wolpaw,et al.  A P300 event-related potential brain–computer interface (BCI): The effects of matrix size and inter stimulus interval on performance , 2006, Biological Psychology.

[23]  Cliff Kushler AAC: Using a Reduced Keyboard. , 1998 .

[24]  Brian Roark,et al.  RSVP keyboard: An EEG based typing interface , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[25]  Robert Gaylord-Ross,et al.  Issues and research in special education , 1992 .

[26]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[27]  S Saxena,et al.  An EMG-controlled grasping system for tetraplegics. , 1995, Journal of rehabilitation research and development.

[28]  S. S. Joshi,et al.  Brain–Muscle–Computer Interface: Mobile-Phone Prototype Development and Testing , 2011, IEEE Transactions on Information Technology in Biomedicine.

[29]  I. Scott MacKenzie,et al.  Measuring errors in text entry tasks: an application of the Levenshtein string distance statistic , 2001, CHI Extended Abstracts.

[30]  Cara E. Stepp,et al.  Discrete Versus Continuous Mapping of Facial Electromyography for Human–Machine Interface Control: Performance and Training Effects , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[31]  Maysam Ghovanloo,et al.  Introduction and preliminary evaluation of the Tongue Drive System: wireless tongue-operated assistive technology for people with little or no upper-limb function. , 2008, Journal of rehabilitation research and development.

[32]  Margrit Betke,et al.  Using kernels for a video-based mouse-replacement interface , 2012, Personal and Ubiquitous Computing.

[33]  E Donchin,et al.  Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[34]  Changmok Choi,et al.  Development and evaluation of a assistive computer interface by SEMG for individuals with spinal cord injuries , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.