Neurophysiological predictors and spectro-spatial discriminative features for enhancing SMR-BCI

• Main Authors: Robinson, Neethu, Thomas, Kavitha Perumpadappil, Vinod, A P
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2020
• Subjects: Engineering::Computer science and engineering; Brain–computer Interfaces; Electroencephalography
• Online Access: https://hdl.handle.net/10356/137223
• Institution: Nanyang Technological University

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.