An efficient multi-scale CNN model with intrinsic feature integration for motor imagery EEG subject classification in brain-machine interfaces. (April 2022)

• Record Type: Journal Article
• Authors: Roy, Arunabha M.
• Is Part Of: Biomedical signal processing and control. Volume 74(2022)
• Publication Date: 2022-04
• Subjects: Brain-computer interfaces (BCIs) -- Motor imagery (MI) -- Electroencephalogram (EEG) -- Signal classification -- Convolutional neural network (CNN) -- Feature extraction; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103496
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: An efficient multi-scale CNN(MS-CNN) model has been proposed with intrinsic feature integration for motor imagery EEG subject classification in brain-machine interfaces. Muti-scale convolution block (MSCB) has been designed which can extract the distinguishable features of several non-overlapping canonical frequency bands of EEG signal from multiple scales, and enhance the overall model performance. It is found that, proposed model records mean average accuracy of 93.74% which is 9.4% average increase (up to 18.1% in subject specific case) compared to the state-of-the art models in BCI competition IV2b dataset. Abstract: Objective: Electroencephalogram (EEG) based motor imagery (MI) classification is an important aspect in brain-machine interfaces (BMIs) which bridges between neural system and computer devices decoding brain signals into recognizable machine commands. However, the MI classification task is challenging due to inherent complex properties, inter-subject variability, and low signal-to-noise ratio (SNR) of EEG signals. To overcome the above-mentioned issues, the current work proposes an efficient multi-scale convolutional neural network (MS-CNN) which can extract the distinguishable features of several non-overlapping canonical frequency bands of EEG signals from multiple scales for MI-BCI classification. Approach: In the framework, discriminant user-specific features have been extracted and integrated to improve the accuracy and performance of the CNN classifier. Additionally, different data augmentation methods have been implemented to further improve the accuracy and robustness of the model. Main results: The model achieves an average classification accuracy of 93.74 % and Cohen's kappa-coefficient of 0.92 on the BCI competition IV2b dataset outperforming several baseline and current state-of-the-art EEG-based MI classification models. Significance: The proposed algorithm effectively addresses the shortcoming of existing CNN-based EEG-MI classification models and significantly improves the classification accuracy. The current framework can provide a stimulus for designing efficient and robust real-time human-robot interaction.