An end-to-end deep learning approach to MI-EEG signal classification for BCIs

By Hauke Dose, Jakob S. Møller, Helle Klingenberg Iversen, Sadasivan K. Puthusserypady. Expert systems with applications, 2018

334 citations recorded by the source corpus.

Abstract Goal: To develop and implement a Deep Learning (DL) approach for an electroencephalogram (EEG) based Motor Imagery (MI) Brain-Computer Interface (BCI) system that could potentially be used to improve the current stroke rehabilitation strategies. Method: The DL model is using Convolutional Neural Network (CNN) layers for learning generalized features and dimension reduction, while a conventional Fully Connected (FC) layer is used for classification. Together they build a unified end-to-end model that can be applied to raw EEG signals. This previously proposed model was applied to a new set of data to validate its robustness against data variations. Furthermore, it was extended by subject-specific adaptation. Lastly, an analysis of the learned filters provides insights into how such a model derives a classification decision. Results: The selected global classifier reached 80.38%, 69.82%, and 58.58% mean accuracies for datasets with two, three, and four classes, respectively, validated using 5-fold crossvalidation. As a novel approach in this context, transfer learning was used to adapt the global classifier to single individuals improving the overall mean accuracy to 86.49%, 79.25%, and 68.51%, respectively. The global models were trained on 3s segments of EEG data from different subjects than they were tested on, which proved the generalization performance of the model. Conclusion: The results are comparable with the reported accuracy values in related studies and the presented model outperforms the results in the literature on the same underlying data. Given that the model can learn features from data without having to use specialized feature extraction methods, DL should be considered as an alternative to established EEG classification methods, if enough data is available.

Subfields: Cardiology and Cardiovascular Medicine, Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/1bfb8e414e9a4a7da2ccc58b1034f021856f0336

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In Cardiology and Cardiovascular Medicine

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, methodology): “[151] applied a deep learning approach to an EEG-basedMI BCI system in healthcare, aiming to enhance the present stroke rehabilitation process.”
Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review (2021, methodology): “The raw EEG signal was used as an input to DL models having a light architecture [107, 109, 110] as well as advanced and compact architectures [108, 115] with little or no preprocessing.”
BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn From Massive Amounts of EEG Data (2021, background): “Our fine-tuned approaches similarly appear reasonably competitive with prior work on the MMI dataset [9, 3], particularly when considering that only 19 channels (rather than the full set of 64) were being used.”