Enhanced inter‐subject brain computer interface with associative sensorimotor oscillations

Abstract

Electroencephalography (EEG) captures electrophysiological signatures of cortical events from the scalp with high-dimensional electrode montages. Usually, excessive sources produce outliers and potentially affect the actual event related sources. Besides, EEG manifests inherent inter-subject variability of the brain dynamics, at the resting state and/or under the performance of task(s), caused probably due to the instantaneous fluctuation of psychophysiological states. A wavelet coherence (WC) analysis for optimally selecting associative inter-subject channels is proposed here and is being used to boost performances of motor imagery (MI)-based inter-subject brain computer interface (BCI). The underlying hypothesis is that optimally associative inter-subject channels can reduce the effects of outliers and, thus, eliminate dissimilar cortical patterns. The proposed approach has been tested on the dataset IVa from BCI competition III, including EEG data acquired from five healthy subjects who were given visual cues to perform 280 trials of MI for the right hand and right foot. Experimental results have shown increased classification accuracy (81.79%) using the WC-based selected 16 channels compared to the one (56.79%) achieved using all the available 118 channels. The associative channels lie mostly around the sensorimotor regions of the brain, reinforced by the previous literature, describing spatial brain dynamics during sensorimotor oscillations. Apparently, the proposed approach paves the way for optimised EEG channel selection that could boost further the efficiency and real-time performance of BCI systems.

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

“…Therefore, machine learning-based BCI models were introduced to reduce individual training session for each BCI use, in which a model has to be calibrated based on the data at the beginning of each session (Ramoser et al, 2000;Blankertz et al, 2002). Recent studies have proposed SMR-based BCI without any session-and subject-specific calibration utilizing the concept of transfer learning (Kang et al, 2009;Li et al, 2010;Lu et al, 2010;Niazi et al, 2013;Kang and Choi, 2014;Fazli et al, 2015;Lotte, 2015;Jayaram et al, 2016;Saha et al, 2017aSaha et al, ,b, 2019Fahimi et al, 2018;He and Wu, 2019).…”

“…While time and effort for building those models could be significantly reduced, they still require training session. Others have recently demonstrated the feasibility of inter-subject BCI models without any training trial from the target subject (Saha et al, 2017a(Saha et al, ,b, 2019. However, the performance requires to be improved significantly prior to real-life use of such BCI systems.…”

“…A transfer learning method is worthwhile if the subjects share non-stationarities that can be modeled in an intersubject context, but ineffective if the subjects exhibit unlike non-stationarities (Samek et al, 2013). 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.…”

“…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).…”

Uma Abordagem Inter-indivíduo para Classificação da Atenção Seletiva Auditiva

“…No entanto, o valor máximo da silhueta média é bem baixo, indicando uma fraca clusterização. Levando-se em conta os dados coletados na abordagem inter-indivíduo e sua grande variabilidade [33], tal resultado aponta a dificuldade da tarefa de classificação. Fig.…”