Online classification of the near-infrared spectroscopy fast optical signal for brain-computer interfaces

Objective. The fast optical signal (FOS), measured with near-infrared spectroscopy (NIRS), has high temporal and competitive spatial resolution which provides an opportunity for a novel brain-computer interface modality. However, the reliability of the FOS has been debated due to its low signal-to-n...

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Bibliographic Details
Published in:Biomedical physics & engineering express 2018-09, Vol.4 (6), p.65010
Main Authors: Proulx, Nicole, Samadani, Ali-Akbar, Chau, Tom
Format: Article
Language:English
Subjects:
brain-computer interface (BCI)
fast optical signal
near-infrared spectroscopy (NIRS)
online classification
prefrontal cortex
visual oddball paradigm
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Summary:Objective. The fast optical signal (FOS), measured with near-infrared spectroscopy (NIRS), has high temporal and competitive spatial resolution which provides an opportunity for a novel brain-computer interface modality. However, the reliability of the FOS has been debated due to its low signal-to-noise ratio. Approach. This study examined the feasibility of automatically classifying the prefrontal FOS response during a visual oddball task. FOS measurements were collected from 15 participants during 3 offline and 2 online sessions. Classification feedback was provided to participants during online sessions. The FOS classification algorithm discriminated between oddball and frequent responses. DC intensity and phase delay FOS measurements were classified individually with both support vector machine (SVM) and linear discriminant analysis (LDA) classifiers. The decisions of these classifiers were combined with a weighted majority vote. Fifteen-trial averages were selected for optimal classification results and the best feature types were found to be the number of zero crossing and the variance. Main results. FOS responses to oddball and frequent images were classified offline with an average balanced accuracy of 62 5% and classified online with an averaged balanced accuracy of 63 6% across all participants. Offline classification accuracies were significantly higher than chance for all participants. Online classification results were significantly above chance in both online sessions for 7 of 14 participants. Event-related potentials (ERPs) were classified using a similar algorithm with an average balanced accuracy of 77 5%, confirming that the prefrontal neuronal response to the visual oddball task could be classified above the level required (>70%) for effective BCI communication. Significance. The FOS classification results demonstrated that automatic classification of the FOS is possible at above-chance levels, however, FOS classification accuracies did not reach the effective BCI communication threshold. Further FOS classification efforts should focus on investigating spectral features as well as adding measurement channels over the fronto-central and parietal areas of the brain.
ISSN:2057-1976
2057-1976
DOI:10.1088/2057-1976/aada1a