Development of a ternary hybrid fNIRS-EEG brain–computer interface based on imagined speech. Issue 4 (2nd October 2019)

• Record Type: Journal Article
• Authors: Rezazadeh Sereshkeh, Alborz; Yousefi, Rozhin; Wong, Andrew T; Rudzicz, Frank; Chau, Tom
• Is Part Of: Brain-computer interfaces. Volume 6:Issue 4(2019)
• Page Start: 128
• Page End: 140
• Publication Date: 2019-10-02
• Subjects: Brain-computer interface -- imagined speech -- hybrid BCI -- fNIRS -- EEG -- regularized linear discriminant analysis (RLDA) -- discrete wavelet transform (DWT); Brain-computer interfaces -- Periodicals; Neurology -- Periodicals; 616.800285
• DOI: 10.1080/2326263X.2019.1698928
• Languages: English
• ISSNs: 2326-263X

Abstract

ABSTRACT: There is increasing interest in developing intuitive brain-computer interfaces (BCIs) to differentiate intuitive mental tasks such as imagined speech. Both electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have been used for this purpose. However, the classification accuracy and number of commands in such BCIs have been limited. The use of multi-modal BCIs to address these issues has been proposed for some common BCI tasks, but not for imagined speech. Here, we propose a multi-class hybrid fNIRS-EEG BCI based on imagined speech. Eleven participants performed multiple iterations of three tasks: mentally repeating 'yes' or 'no' for 15 s or an equivalent duration of unconstrained rest. We achieved an average ternary classification accuracy of 70.45 ± 19.19% which is significantly better than that attained with each modality alone (p < 0.05). Our findings suggest that concurrent measurements of EEG and fNIRS can improve classification accuracy of BCIs based on imagined speech.