An Approach of One-vs-Rest Filter Bank Common Spatial Pattern and Spiking Neural Networks for Multiple Motor Imagery Decoding

Abstract

Motor imagery (MI) is a typical BCI paradigm and has been widely applied into many aspects (e.g. brain-driven wheelchair and motor function rehabilitation training). Although significant achievements have been achieved, multiple motor imagery decoding is still unsatisfactory. To deal with this challenging issue, firstly, a segment of electroencephalogram was extracted and preprocessed. Secondly, we applied a filter bank common spatial pattern (FBCSP) with one-vs-rest (OVR) strategy to extract the spatio-temporal-frequency features of multiple MI. Thirdly, the F-score was employed to optimise and select these features. Finally, the optimized features were fed to the spiking neural networks (SNN) for classification. Evaluation was conducted on two public multiple MI datasets (Dataset IIIa of the BCI competition III and Dataset IIa of the BCI competition IV). Experimental results showed that the average accuracy of the proposed framework reached up to 90.09% (kappa: 0.868) and 81.33% (kappa: 0.751) on the two public datasets, respectively. The achieved performance (accuracy and kappa) was comparable to the best one of the compared methods. This study demonstrated that the proposed method can be used as an alternative approach for multiple MI decoding and it provided a potential solution for online multiple MI detection. INDEX TERMS Electroencephalogram, motor imagery (MI), filter bank common spatial pattern (FBCSP), spiking neural networks (SNN). I. INTRODUCTION Electroencephalography (EEG) signal is usually used in brain-computer interface (BCI) systems due to its high temporal resolution [1]. Motor imagery (MI)-based BCI is one of classical paradigms and has been employed to restore the communication pathway or movement function for disabled, paralyzed, and stroke patients [2], [3]. Patients are able to The associate editor coordinating the review of this manuscript and approving it for publication was Li He .

A Temporal-Spectral-Based Squeeze-and- Excitation Feature Fusion Network for Motor Imagery EEG Decoding

“…Sakhavi et al [15] utilized a FBCSP to generate temporal representations of EEG which were then fed into a CNN for the EEG classification. In addition, the spiking neural network was used [16] and combined with OVR (One-Vs-Rest) FBCSP which extracted temporal-frequency features from multiple MI-EEG. Li et al [14] further investigated multi-domain representation of EEG and performed spectral and temporal analysis together with a designed CNN.…”

Diverse Feature Blend Based on Filter-Bank Common Spatial Pattern and Brain Functional Connectivity for Multiple Motor Imagery Detection

“…The selection of the time window can remove the segmentation of EEG signals that have nothing to do with MI or eliminate data that is not the key time point of MI [27]. Our previous study has also shown that choosing the optimal time window for each subject can indeed improve the classification accuracy [40]. Although the optimal time windows of each subject's MI are different, it is found that the time between 0-2.5 s after the onset of visual cue can benefit the classification considering adequate sample points for subsequent data processing [41].…”

Covariation Informed Graph Slepians for Motor Imagery Decoding

“…In the following years, several variations/modifications of the original algorithm were proposed (e.g. [23]- [26]) that led to This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ more robust MI-decoding schemes.…”