Wavelet Entropy-Based Inter-subject Associative Cortical Source Localization for Sensorimotor BCI

• Main Authors: Simanto Saha, Md. Shakhawat Hossain, Khawza Ahmed, Raqibul Mostafa, Leontios Hadjileontiadis, Ahsan Khandoker, Mathias Baumert
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2019-07-01
• Series: Frontiers in Neuroinformatics
• Subjects: inter-subject sensorimotor dynamics; brain computer interface; wavelet based maximum entropy on the mean; motor imagery; electroencephalography
• Online Access: https://www.frontiersin.org/article/10.3389/fninf.2019.00047/full

We propose event-related cortical sources estimation from subject-independent electroencephalography (EEG) recordings for motor imagery brain computer interface (BCI). By using wavelet-based maximum entropy on the mean (wMEM), task-specific EEG channels are selected to predict right hand and right foot sensorimotor tasks, employing common spatial pattern (CSP) and regularized common spatial pattern (RCSP). EEG from five healthy individuals (Dataset IVa, BCI Competition III) were evaluated by a cross-subject paradigm. Prediction performance was evaluated via a two-layer feed-forward neural network, where the classifier was trained and tested by data from two subjects independently. On average, the overall mean prediction accuracies obtained using all 118 channels are (55.98±6.53) and (71.20±5.32) in cases of CSP and RCSP, respectively, which are slightly lower than the accuracies obtained using only the selected channels, i.e., (58.95±6.90) and (71.41±6.65), respectively. The highest mean prediction accuracy achieved for a specific subject pair by using selected EEG channels was on average (90.36±5.59) and outperformed that achieved by using all available channels (86.07 ± 10.71). Spatially projected cortical sources approximated using wMEM may be useful for capturing inter-subject associative sensorimotor brain dynamics and pave the way toward an enhanced subject-independent BCI.