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Sensors 2018, 18(9), 2989; https://doi.org/10.3390/s18092989

Classification of Overt and Covert Speech for Near-Infrared Spectroscopy-Based Brain Computer Interface

1
Centre for Robotics Research, Department of Informatics, King’s College London, London WC2B 4BG, UK
2
Basque Center on Cognition, Brain and Language, 20009 Donostia, Spain
3
Department of Robotics and Artificial Intelligence, National University of Sciences and Technology, Islamabad 24090, Pakistan
4
Department of Electrical Engineering, Faculty of Engineering, Islamic University Medina, Al Jamiah 42351, Saudi Arabia
5
Center for Chiropractic Research, New Zealand College of Chiropractic, Auckland 1010, New Zealand
6
SMI, Department of Health Science and Technology, Aalborg University, 9100 Aalborg, Denmark
7
Health and Rehabilitation Research Institute, AUT University, Auckland 1010, New Zealand
*
Author to whom correspondence should be addressed.
Received: 17 July 2018 / Revised: 17 August 2018 / Accepted: 5 September 2018 / Published: 7 September 2018
(This article belongs to the Special Issue Biomedical Infrared Imaging: From Sensors to Applications)
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Abstract

People suffering from neuromuscular disorders such as locked-in syndrome (LIS) are left in a paralyzed state with preserved awareness and cognition. In this study, it was hypothesized that changes in local hemodynamic activity, due to the activation of Broca’s area during overt/covert speech, can be harnessed to create an intuitive Brain Computer Interface based on Near-Infrared Spectroscopy (NIRS). A 12-channel square template was used to cover inferior frontal gyrus and changes in hemoglobin concentration corresponding to six aloud (overtly) and six silently (covertly) spoken words were collected from eight healthy participants. An unsupervised feature extraction algorithm was implemented with an optimized support vector machine for classification. For all participants, when considering overt and covert classes regardless of words, classification accuracy of 92.88 ± 18.49% was achieved with oxy-hemoglobin (O2Hb) and 95.14 ± 5.39% with deoxy-hemoglobin (HHb) as a chromophore. For a six-active-class problem of overtly spoken words, 88.19 ± 7.12% accuracy was achieved for O2Hb and 78.82 ± 15.76% for HHb. Similarly, for a six-active-class classification of covertly spoken words, 79.17 ± 14.30% accuracy was achieved with O2Hb and 86.81 ± 9.90% with HHb as an absorber. These results indicate that a control paradigm based on covert speech can be reliably implemented into future Brain–Computer Interfaces (BCIs) based on NIRS. View Full-Text
Keywords: brain computer interface; near infrared spectroscopy; overt and covert speech; unsupervised feature extraction; Broca’s area; decoding speech brain computer interface; near infrared spectroscopy; overt and covert speech; unsupervised feature extraction; Broca’s area; decoding speech
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Kamavuako, E.N.; Sheikh, U.A.; Gilani, S.O.; Jamil, M.; Niazi, I.K. Classification of Overt and Covert Speech for Near-Infrared Spectroscopy-Based Brain Computer Interface. Sensors 2018, 18, 2989.

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