Deep learning-based electroencephalography analysis: a systematic review
Open accessAbstract
CONTEXT: Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question. OBJECTIVE: In this work, we review 154 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, brain-computer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches from this large body of literature in order to inform future research and formulate recommendations. METHODS: Major databases spanning the fields of science and engineering were queried to identify relevant studies published in scientific journals, conferences, and electronic preprint repositories. Various data items were extracted for each study pertaining to (1) the data, (2) the preprocessing methodology, (3) the DL design choices, (4) the results, and (5) the reproducibility of the experiments. These items were then analyzed one by one to uncover trends. RESULTS: Our analysis reveals that the amount of EEG data used across studies varies from less than ten minutes to thousands of hours, while the number of samples seen during training by a network varies from a few dozens to several millions, depending on how epochs are extracted. Interestingly, we saw that more than half the studies used publicly available data and that there has also been a clear shift from intra-subject to inter-subject approaches over the last few years. About [Formula: see text] of the studies used convolutional neural networks (CNNs), while [Formula: see text] used recurrent neural networks (RNNs), most often with a total of 3-10 layers. Moreover, almost one-half of the studies trained their models on raw or preprocessed EEG time series. Finally, the median gain in accuracy of DL approaches over traditional baselines was [Formula: see text] across all relevant studies. More importantly, however, we noticed studies often suffer from poor reproducibility: a majority of papers would be hard or impossible to reproduce given the unavailability of their data and code. SIGNIFICANCE: To help the community progress and share work more effectively, we provide a list of recommendations for future studies and emphasize the need for more reproducible research. We also make our summary table of DL and EEG papers available and invite authors of published work to contribute to it directly. A planned follow-up to this work will be an online public benchmarking portal listing reproducible results.
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Handcrafted approaches require task-specific processing pipelines, limiting the generalization ability and flexibility of EEG-BCI.
“It requires task-specific processing pipelines, limiting the generalization ability and flexibility of EEG-BCI [2].”
From IENet: a robust convolutional neural network for EEG based brain-computer interfaces (2022) · cites this paper as [2]
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“EEG based BCI (EEG-BCI) has been applied widely in the clinical field due to its non-invasiveness, high temporal resolution, low cost and other ideal characteristics [2, 3].”
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“Roy et al. (2019) swap left–right electrodes within the same class, yielding modest benefits.”
- Authors
- Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T. H., & Faubert, J.
- Affiliations
- Université de Montréal (CA)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Université du Québec à Montréal (CA)
- Corresponding author
- Roy, Yannick
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2019-08-14
- Volume
- 16
- Issue
- 5
- Pages
- 051001
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W2909338255
- Cited by
- 1,532
- References
- 24
- Citations by year
- 2026: 296 · 2025: 274 · 2024: 170 · 2023: 219 · 2022: 203 · 2021: 213 · 2020: 128 · 2019: 29
- FWCI
- 18.19
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion)
- Oskar-Bordeaux (Universite de Bordeaux) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Natural Sciences and Engineering Research Council of Canada (RDPJ 514052-17)
- Fonds de Recherche du Québec—Nature et Technologies
- InteraXon Inc.
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- EEG, deep learning, EEG signal processing, convolutional neural networks, recurrent neural networks, reproducibility, epilepsy, sleep, brain-computer interface, EEG preprocessing, research trends, research recommendations
- MeSH terms
- Deep Learning, Brain, Electroencephalography, Humans, Databases, Factual, Brain-Computer Interfaces
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