mlsanityData to 7 Oct 2026

A Strong and Simple Deep Learning Baseline for BCI MI Decoding

arXiv 2309.07159 ↗First posted 11 Sep 2023eess.SP cs.LG q-bio.NCcited by 5

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Abstract (arXiv)

We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI. Our main motivation is to propose a simple and performing baseline to compare to, using only very standard ingredients from the literature. We evaluate its performance on four EEG Motor Imagery datasets, including simulated online setups, and compare it to recent Deep Learning and Machine Learning approaches. EEG-SimpleConv is at least as good or far more efficient than other approaches, showing strong knowledge-transfer capabilities across subjects, at the cost of a low inference time. We advocate that using off-the-shelf ingredients rather than coming with ad-hoc solutions can significantly help the adoption of Deep Learning approaches for BCI. We make the code of the models and the experiments accessible.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 25 of its 64 references that are in the map. First posted on arXiv 11 Sep 2023.

Builds on

25 of 64 references.

References · 25

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13 more
  1. 2017Interpretable Convolutional Neural Networkscited by 161
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  3. 2021Transformer-based Spatial-Temporal Feature Learning for EEG Decodingcited by 92
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  8. 2022Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classificationcited by 21
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Cited by year

Citing papers per year: 2024: 4; 2025: 1.

5 papers.

  1. 2024A Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decodingcited by 10
  2. 2024Geometric Neural Network based on Phase Space for BCI-EEG decodingcited by 5
  3. 2024Unsupervised Adaptive Deep Learning Method For BCI Motor Imagery Decodingcited by 3
  4. 2025Tailoring deep learning for real-time brain-computer interfaces: From offline models to calibration-free online decodingcited by 1
  5. 2024Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networkscited by 0