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

Abstract

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.

Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review

“…The term inter-subject associativity refers to potential inter-subject BCI performance predictors, which could be incorporated into BCI design to augment transfer learning (Kang and Choi, 2014;Wronkiewicz et al, 2015;Saha et al, 2017aSaha et al, ,b, 2019. Source-space analysis for detecting inter-subject associative EEG channels can improve SMR-based BCI performance (Wronkiewicz et al, 2015;Saha et al, 2017aSaha et al, , 2019. For example, the classification accuracies for two different subject pairs are 90.36 ± 5.59% and 63.21 ± 8.43%, respectively, suggesting not both subject pairs can be used to achieve a good performance (Saha et al, 2019).…”

“…Source-space analysis for detecting inter-subject associative EEG channels can improve SMR-based BCI performance (Wronkiewicz et al, 2015;Saha et al, 2017aSaha et al, , 2019. For example, the classification accuracies for two different subject pairs are 90.36 ± 5.59% and 63.21 ± 8.43%, respectively, suggesting not both subject pairs can be used to achieve a good performance (Saha et al, 2019).…”

“…Recent advances in deep learning methods demonstrate a potential application that alleviates intraand inter-subject variability in BCI settings (Chiarelli et al, 2018;Fahimi et al, 2018). Meanwhile, recent studies suggest that the quantification of inter-subject associativity could be equally important to increase the efficacy of exclusively machine learning-based transfer learning strategies for covariate shift adaptation (Kang et al, 2009;Kang and Choi, 2014;Wronkiewicz et al, 2015;Saha et al, 2017bSaha et al, , 2019Perdikis et al, 2018).…”

Deep Learning Based Inter-subject Continuous Decoding of Motor Imagery for Practical Brain-Computer Interfaces

“…Previous attempts to solve this problem involved (1) attempting to discover globally relevant EEG features and (2) the use of adaptive EEG classifiers (Lotte et al, 2018 ). Recent studies also utilized some BCI performance Predictors to augment the transfer learning process (Saha et al, 2018 ; Saha et al, 2019 ).…”

“…Another popular method of intra- and inter-subject transfer learning is covariate shift adaptation by combining the unlabeled test data with the labeled training data which corrects the covariate shifts arising from the changes of marginal distribution between different subjects/sessions (Li et al, 2010 ; Arvaneh et al, 2014 ). Some different approaches are also proposed for inter-subject transfer learning where event-related cortical sources are estimated from subject independent EEG recordings (Saha et al, 2019 ) which can compensate for the changes in head morphology and electrode positioning (Wronkiewicz et al, 2015 ). In a recent study, a Riemannian geometry-based approach is successfully applied for cross-subject and cross-session transfer learning which significantly improved BCI performance (Zanini et al, 2018 ; Gaur et al, 2019a ).…”