Brain–Computer Interface (BCI) system offers a new means of communication for those with paralysis or severe neuromuscular disorders. BCI systems based on Motor Imagery (MI) Electroencephalography (EEG) signals enable the user to convert their thoughts into actions without any voluntary muscle movement. Recently, Convolutional neural network (CNN) is used for the classification of MI signals. However, to produce good MI classification, it is necessary to effectively represent the signal as an input image to the CNN and train the deep learning classifier using large training data.
In this work, EEG signals are acquired over 16 channels and are filtered using a bandpass filter with the frequency range of 1 to 100 Hz. The processed signal is spatially filtered using Common Spatial Pattern (CSP) filter. The spectrograms of the spatially filtered signals are given as input to CNN. A single convolutional layer CNN is designed to classify left hand, right hand, both hands, and feet MI EEG signals. The size of the training data is increased by augmenting the spectrograms of the EEG signals.
The CNN classifier was evaluated using MI signals acquired from twelve healthy subjects. Results show that the proposed method achieved an average classification accuracy of 95.18 ± 2.51% for two-class (left hand and right hand) and 87.37 ± 1.68% for four-class (Left hand, Right hand, Both hands, and Feet) MI.
Thus, the method manifests that this 2D representation of 1D EEG signal along with image augmentation shows a high potential for classification of MI EEG signals using the designed CNN model.
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We wish to inform that this work was supported by the Life Science Research Board (LSRB), Defence Research and Development Organization (DRDO) under the Grants-in-Aid scheme (Grant No. LSRB-291/LS&BD/2017). Nijisha Shajil would like to thank the Department of Science and Technology (DST), India for funding her research through INSPIRE fellowship (IF180459).
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The authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the University and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
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Shajil, N., Mohan, S., Srinivasan, P. et al. Multiclass Classification of Spatially Filtered Motor Imagery EEG Signals Using Convolutional Neural Network for BCI Based Applications. J. Med. Biol. Eng. (2020). https://doi.org/10.1007/s40846-020-00538-3
- Brain-computer interface
- EEG data
- Motor imagery
- Convolutional neural network
- Common spatial pattern