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CNN based classification of motor imaginary using variational mode decomposed EEG-spectrum image

Publication Type : Journal Article

Publisher : Biomedical Engineering Letters

Source : Biomedical Engineering Letters (2021)

Url : https://doi.org/10.1007/s13534-021-00190-z

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Electronics and Communication

Year : 2021

Abstract : A novel approach of preprocessing EEG signals by generating spectrum image for effective Convolutional Neural Network (CNN) based classification for Motor Imaginary (MI) recognition is proposed. The approach involves extracting the Variational Mode Decomposition (VMD) modes of EEG signals, from which the Short Time Fourier Transform (STFT) of all the modes are arranged to form EEG spectrum images. The EEG spectrum images generated are provided as input image to CNN. The two generic CNN architectures for MI classification (EEGNet and DeepConvNet) and the architectures for pattern recognition (AlexNet and LeNet) are used in this study. Among the four architectures, EEGNet provides average accuracies of 91.37%, 94.41%, 85.67% and 90.21% for the four datasets used to validate the proposed approach. Consistently better results in comparison with results in recent literature demonstrate that the EEG spectrum image generation using VMD-STFT is a promising method for the time frequency analysis of EEG signals.

Cite this Research Publication : K. K. Krishnan and Dr. Soman K. P., “CNN based classification of motor imaginary using variational mode decomposed EEG-spectrum image”, Biomedical Engineering Letters, 2021.

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