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An in-depth survey on Deep Learning-based Motor Imagery Electroencephalogram (EEG) classification.
Wang, Xianheng; Liesaputra, Veronica; Liu, Zhaobin; Wang, Yi; Huang, Zhiyi.
Afiliación
  • Wang X; Department of Computer Science, University of Otago, Dunedin, New Zealand. Electronic address: wanhe541@student.otago.ac.nz.
  • Liesaputra V; Department of Computer Science, University of Otago, Dunedin, New Zealand. Electronic address: veronica.liesaputra@otago.ac.nz.
  • Liu Z; College of Information Science and Technology, Dalian Maritime University, Liaoning, PR China.
  • Wang Y; Laboratory for Circuit and Behavioral Physiology, RIKEN Center for Brain Science, Wako-shi, Saitama, Japan.
  • Huang Z; Department of Computer Science, University of Otago, Dunedin, New Zealand. Electronic address: zhiyi.huang@otago.ac.nz.
Artif Intell Med ; 147: 102738, 2024 01.
Article en En | MEDLINE | ID: mdl-38184362
ABSTRACT
Electroencephalogram (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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Interfaces Cerebro-Computador / Aprendizaje Profundo Tipo de estudio: Prognostic_studies / Qualitative_research Límite: Humans Idioma: En Revista: Artif Intell Med Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Interfaces Cerebro-Computador / Aprendizaje Profundo Tipo de estudio: Prognostic_studies / Qualitative_research Límite: Humans Idioma: En Revista: Artif Intell Med Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos