Feature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces.

In this study, a brain-computer interface (BCI) framework for hybrid functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) for locked-in syndrome (LIS) patients is investigated. Brain tasks, channel selection methods, and feature extraction and classification algorithms available in the literature are reviewed. First, we categorize various types of patients with cognitive and motor impairments to assess the suitability of BCI for each of them. The prefrontal cortex is identified as a suitable brain region for imaging. Second, the brain activity that contributes to the generation of hemodynamic signals is reviewed. Mental arithmetic and word formation tasks are found to be suitable for use with LIS patients. Third, since a specific targeted brain region is needed for BCI, methods for determining the region of interest are reviewed. The combination of a bundled-optode configuration and threshold-integrated vector phase analysis turns out to be a promising solution. Fourth, the usable fNIRS features and EEG features are reviewed. For hybrid BCI, a combination of the signal peak and mean fNIRS signals and the highest band powers of EEG signals is promising. For classification, linear discriminant analysis has been most widely used. However, further research on vector phase analysis as a classifier for multiple commands is desirable. Overall, proper brain region identification and proper selection of features will improve classification accuracy. In conclusion, five future research issues are identified, and a new BCI scheme, including brain therapy for LIS patients and using the framework of hybrid fNIRS-EEG BCI, is provided.