dc.contributor.author | Kamavuako, Ernest Nlandu | |
dc.contributor.author | Sheikh, Usman Ayub | |
dc.contributor.author | Gilani, Syed Omer | |
dc.contributor.author | Jamil, Mohsin | |
dc.contributor.author | Niazi, Imran Khan | |
dc.date.accessioned | 2018-09-12T11:14:25Z | |
dc.date.available | 2018-09-12T11:14:25Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Kamavuako, E.N., Sheikh, U.A., Gilani, S.O., Jamil, M., & Niazi, I.K. (2018). Classification of Overt and Covert Speech for Near-Infrared Spectroscopy-Based Brain Computer Interface. Sensors, 18, 2989. Doi:10.3390/s18092989 | es_ES |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/10810/28663 | |
dc.description | Published: 7 September 2018 | es_ES |
dc.description.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 | es_ES |
dc.description.sponsorship | This research received no external funding. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Sensors | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.subject | brain computer interface | es_ES |
dc.subject | near infrared spectroscopy | es_ES |
dc.subject | overt and covert speech | es_ES |
dc.subject | unsupervised feature extraction | es_ES |
dc.subject | Broca’s area | es_ES |
dc.subject | decoding speech | es_ES |
dc.title | Classification of Overt and Covert Speech for Near-Infrared Spectroscopy-Based Brain Computer Interface | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/). | es_ES |
dc.relation.publisherversion | http://www.mdpi.com/journal/sensors | es_ES |
dc.identifier.doi | 10.3390/s18092989 | |