The DOI matches its Crossref record; 110 of the 110 references it cites were found in a registry or an index.
Deep learning for motor imagery EEG-based classification: A review
Abstract
Abstract Objectives The availability of large and varied Electroencephalogram (EEG) datasets, rapidly advances and inventions in deep learning techniques, and highly powerful and diversified computing systems have all permitted to easily analyzing those datasets and discovering vital information within. However, the classification process of EEG signals and discovering vital information should be robust, automatic, and with high accuracy. Motor Imagery (MI) EEG has attracted us due to its significant applications in daily life. Methods This paper attempts to achieve those goals throughout a systematic review of the state-of-the-art studies within this field of research. The process began by intensely surfing the well-known specialized digital libraries and, as a result, 40 related papers were gathered. The papers were scrutinized upon multiple noteworthy technical issues, among them deep neural network architecture, input formulation, number of MI EEG tasks, and frequency range of interest. Outcomes Deep neural networks build robust and automated systems for the classification of MI EEG recordings by exploiting the whole input data throughout learning salient features. Specifically, convolutional neural networks (CNN) and hybrid-CNN (h-CNN) are the dominant architectures with high performance in comparison to public datasets with other types of architectures. The MI related datasets, input formulation, frequency ranges, and preprocessing and regularization methods were also reviewed. Inferences This review gives the required preliminaries in developing MI EEG-based BCI systems. The review process of the published articles in the last five years aims to help in choosing the appropriate deep neural network architecture and other hyperparameters for developing those systems.
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A survey on deep learning in medical image analysis
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EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
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Deep learning with convolutional neural networks for EEG decoding and visualization
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Deep learning for electroencephalogram (EEG) classification tasks: a review
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A State-of-the-Art Survey on Deep Learning Theory and Architectures
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Review of the BCI Competition IV
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The BCI competition III: validating alternative approaches to actual BCI problems
Blankertz, B., Muller, K.-R., Krusienski, D. J., Schalk, G., Wolpaw, J. R., Schlogl, A., Pfurtscheller, G., Millan, J. R., Schroder, M., & Birbaumer, N. (2006) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2006.875642 · Cited by 927
A novel deep learning approach for classification of EEG motor imagery signals
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Neural Decoding of EEG Signals with Machine Learning: A Systematic Review
Saeidi, M., Karwowski, W., Farahani, F. V., Fiok, K., Taiar, R., Hancock, P. A., & Al-Juaid, A. (2021) · Brain Sciences · doi:10.3390/brainsci11111525 · Cited by 258
EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification
Salami, A., Andreu-Perez, J., & Gillmeister, H. (2022) · IEEE Access · doi:10.1109/access.2022.3161489 · Cited by 155
A State-of-the-Art Review of EEG-Based Imagined Speech Decoding
Lopez-Bernal, D., Balderas, D., Ponce, P., & Molina, A. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.867281 · Cited by 107
IENet: a robust convolutional neural network for EEG based brain-computer interfaces
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Metadata
- Authors
- Al-Saegh, A., Dawwd, S. A., & Abdul-Jabbar, J. M.
- Venue
- Biomedical Signal Processing and Control
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 63
- Pages
- 102172
- ISSN
- 1746-8094 (ISSN-L)
- OpenAlex
- W3092342532
- Cited by
- 437
- References
- 110
- FWCI
- 23.16
- Type
- journal article
- Language
- en
- Open access
- closed
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