Neurophysiological predictors and spectro-spatial discriminative features for enhancing SMR-BCI. (31st October 2018)

• Record Type: Journal Article
• Authors: Robinson, Neethu; Thomas, Kavitha P; Vinod, A P
• Is Part Of: Journal of neural engineering. Volume 15:Number 6(2018:Dec.)
• Publication Date: 2018-10-31
• Subjects: brain–computer interfaces -- electroencephalography -- discriminative spectro-spatial patterns -- neurophysiological predictors; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/aae597
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Neural engineering research is actively engaged in optimizing the robustness of sensorimotor rhythms (SMR)—brain–computer interface (BCI) to boost its potential real-world use. Objective . This paper investigates two vital factors in efficient and robust SMR-BCI design—algorithms that address subject-variability of optimal features and neurophysiological factors that correlate with BCI performance. Existing SMR-BCI research using electroencephalogram (EEG) to classify bilateral motor imagery (MI) focus on identifying subject-specific frequency bands with most discriminative motor patterns localized to sensorimotor region. Approach . A novel strategy to further optimize BCI performance by taking into account the variability of discriminative spectral regions across various EEG channels is proposed in this paper. Main results . The proposed technique results in a significant ( ) increase in average ( ) classification accuracy by accompanied by a considerable reduction in number of channels and bands. The session-to-session transfer variation in spectro-spatial patterns using proposed algorithm is investigated offline and classification performance of the optimized BCI model is successfully evaluated in an online SMR-BCI. Further, the effective prediction of SMR-BCI performance with physiological indicators derived from multi-channel resting-state EEG is demonstrated. The results indicate that the resting state activation patterns such as entropy and gamma power from pre-motor (fronto-central) and posterior (parietal and centro-parietal) areas, and beta power from posterior (centro-parietal) areas estimate BCI performance with minimum error. These patterns, strongly related to BCI performance, may represent certain cognitive states during rest. Significance . Findings reported in this paper imply the need for subject-specific modelling of BCI and the prediction of BCI performance using multi-channel rest-state parameters, to ensure enhanced BCI performance.