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Deep Learning based Inter-subject Continuous Decoding of Motor Imagery for Practical Brain-Computer Interfaces

Roy, Sujit and Chowdhury, Anirban and McCreadie, Karl and Prasad, Girijesh (2020) 'Deep Learning based Inter-subject Continuous Decoding of Motor Imagery for Practical Brain-Computer Interfaces.' Frontiers in Neuroscience, 14. 918-. ISSN 1662-453X

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Inter-subject transfer learning is a long-standing problem in brain-computer interfaces (BCIs) and has not yet been fully realized due to high inter-subject variability in the brain signals related to motor imagery (MI). The recent success of deep learning-based algorithms in classifying different brain signals warrants further exploration to determine whether it is feasible for the inter-subject continuous decoding of MI signals to provide contingent neurofeedback which is important for neurorehabilitative BCI designs. In this paper, we have shown how a convolutional neural network (CNN) based deep learning framework can be used for inter-subject continuous decoding of MI related electroencephalographic (EEG) signals using the novel concept of Mega Blocks for adapting the network against inter-subject variabilities. These Mega Blocks have the capacity to repeat a specific architectural block several times such as one or more convolutional layers in a single Mega Block. The parameters of such Mega Blocks can be optimized using Bayesian hyperparameter optimization. The results, obtained on the publicly available BCI competition IV-2b dataset, yields an average inter-subject continuous decoding accuracy of 71.49% (kappa=0.42) and 70.84% (kappa =0.42) for two different training methods such as adaptive moment estimation (Adam) and stochastic gradient descent (SGDM) respectively in 7 out of 9 subjects. Our results show for the first time that it is feasible to use CNN based architectures for inter-subject continuous decoding with a sufficient level of accuracy for developing calibration-free MI-BCIs for practical purposes.

Item Type: Article
Uncontrolled Keywords: Convolutional neural network (CNN); deep learning; Motor Imagery; brain-computer interface (BCI); electroencephalography (EEG); Adaptive Learning; SGDM; ADAM
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 01 Oct 2020 12:16
Last Modified: 18 Aug 2022 10:40

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