Toward Real-World BCI: CCSPNet, A Compact Subject-Independent Motor Imagery Framework

• URL: http://arxiv.org/abs/2012.13567v3
• Date: Sat, 10 Apr 2021 19:10:36 GMT
• Title: Toward Real-World BCI: CCSPNet, A Compact Subject-Independent Motor Imagery Framework
• Authors: Mahbod Nouri, Faraz Moradi, Hafez Ghaemi, Ali Motie Nasrabadi
• Abstract summary: A conventional brain-computer interface (BCI) requires a complete data gathering, training, and calibration phase for each user before it can be used. We propose a novel subject-independent BCI framework named CCSPNet that is trained on the motor imagery (MI) paradigm of a large-scale EEG signals database. The proposed framework applies a wavelet kernel convolutional neural network (WKCNN) and a temporal convolutional neural network (TCNN) in order to represent and extract the diverse spectral features of EEG signals.
• Score: 2.0741711594051377
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: A conventional brain-computer interface (BCI) requires a complete data gathering, training, and calibration phase for each user before it can be used. This preliminary phase is time-consuming and should be done under the supervision of technical experts commonly in laboratories for the BCI to function properly. In recent years, a number of subject-independent (SI) BCIs have been developed. However, there are many problems preventing them from being used in real-world BCI applications. A weaker performance compared to the subject-dependent (SD) approach and a relatively large number of model parameters are the most important ones. Therefore, a real-world BCI application would greatly benefit from a compact subject-independent BCI framework, ready to be used immediately after the user puts it on, and suitable for low-power edge-computing and applications in the emerging area of internet of things (IoT). In this work, we propose a novel subject-independent BCI framework named CCSPNet (Convolutional Common Spatial Pattern Network) that is trained on the motor imagery (MI) paradigm of a large-scale EEG signals database consisting of 400 trials for every 54 subjects performing two-class hand-movement MI tasks. The proposed framework applies a wavelet kernel convolutional neural network (WKCNN) and a temporal convolutional neural network (TCNN) in order to represent and extract the diverse spectral features of EEG signals. The outputs of the convolutional layers go through a common spatial pattern (CSP) algorithm for spatial feature extraction. The number of CSP features is reduced by a dense neural network, and the final class label is determined by a linear discriminative analysis (LDA). The CCSPNet framework evaluation results show that it is possible to have a low-power compact BCI that achieves both SD and SI performance comparable to complex and computationally expensive models.

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