An in-depth survey on Deep Learning-based Motor Imagery Electroencephalogram (EEG) classification

dc.citation.issueJanuary 2024
dc.citation.volume147
dc.contributor.authorWang X
dc.contributor.authorLiesaputra V
dc.contributor.authorLiu Z
dc.contributor.authorWang Y
dc.contributor.authorHuang Z
dc.date.accessioned2025-01-13T20:34:38Z
dc.date.available2025-01-13T20:34:38Z
dc.date.issued2024-01
dc.description.abstractElectroencephalogram (EEG)-based Brain–Computer Interfaces (BCIs) build a communication path between human brain and external devices. Among EEG-based BCI paradigms, the most commonly used one is motor imagery (MI). As a hot research topic, MI EEG-based BCI has largely contributed to medical fields and smart home industry. However, because of the low signal-to-noise ratio (SNR) and the non-stationary characteristic of EEG data, it is difficult to correctly classify different types of MI-EEG signals. Recently, the advances in Deep Learning (DL) significantly facilitate the development of MI EEG-based BCIs. In this paper, we provide a systematic survey of DL-based MI-EEG classification methods. Specifically, we first comprehensively discuss several important aspects of DL-based MI-EEG classification, covering input formulations, network architectures, public datasets, etc. Then, we summarize problems in model performance comparison and give guidelines to future studies for fair performance comparison. Next, we fairly evaluate the representative DL-based models using source code released by the authors and meticulously analyse the evaluation results. By performing ablation study on the network architecture, we found that (1) effective feature fusion is indispensable for multi-stream CNN-based models. (2) LSTM should be combined with spatial feature extraction techniques to obtain good classification performance. (3) the use of dropout contributes little to improving the model performance, and that (4) adding fully connected layers to the models significantly increases their parameters but it might not improve their performance. Finally, we raise several open issues in MI-EEG classification and provide possible future research directions.
dc.description.confidentialfalse
dc.identifier.citationWang X, Liesaputra V, Liu Z, Wang Y, Huang Z. (2024). An in-depth survey on Deep Learning-based Motor Imagery Electroencephalogram (EEG) classification. Artificial Intelligence in Medicine. 147. January 2024.
dc.identifier.doi10.1016/j.artmed.2023.102738
dc.identifier.eissn1873-2860
dc.identifier.elements-typejournal-article
dc.identifier.issn0933-3657
dc.identifier.number102738
dc.identifier.piiS093336572300252X
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/72360
dc.languageEnglish
dc.publisherElsevier BV, Netherlands
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S093336572300252X
dc.relation.isPartOfArtificial Intelligence in Medicine
dc.rights(c) 2023 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectclassification
dc.subjectDeep learning
dc.subjectMotor imagery electroencephalogram
dc.subjectSurvey
dc.titleAn in-depth survey on Deep Learning-based Motor Imagery Electroencephalogram (EEG) classification
dc.typeJournal article
pubs.elements-id493309
pubs.organisational-groupOther
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