# Transfer Learning between Motor Imagery Datasets using Deep Learning -- Validation of Framework and Comparison of Datasets

arXiv: https://arxiv.org/abs/2311.16109 · HTML: https://mlsanity.com/paper/2311.16109/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 4 Sep 2023.

## Builds on (most cited first, 6 of 6)

- [MOABB: Trustworthy algorithm benchmarking for BCIs](https://mlsanity.com/paper/1805.06427/) (2018)
- [SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG](https://mlsanity.com/paper/2206.01323/) (2022)
- [Transfer Learning in Brain-Computer Interfaces](https://mlsanity.com/paper/1512.00296/) (2015)
- [2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets](https://mlsanity.com/paper/2202.12950/) (2022)
- [Q-EEGNet: an Energy-Efficient 8-bit Quantized Parallel EEGNet Implementation for Edge Motor-Imagery Brain--Machine Interfaces](https://mlsanity.com/paper/2004.11690/) (2020)
- [Evaluating the structure of cognitive tasks with transfer learning](https://mlsanity.com/paper/2308.02408/) (2023)

## Built on by (most cited first, 4 of 4)

- [The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark](https://mlsanity.com/paper/2404.15319/) (2024)
- [A Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding](https://mlsanity.com/paper/2401.10746/) (2024)
- [Tailoring deep learning for real-time brain-computer interfaces: From offline models to calibration-free online decoding](https://mlsanity.com/paper/2507.06779/) (2025)
- [Learning aligned EEG representations with subject-specific encoders](https://mlsanity.com/paper/2606.16462/) (2026)

## Abstract (arXiv)

We present a simple deep learning-based framework commonly used in computer vision and demonstrate its effectiveness for cross-dataset transfer learning in mental imagery decoding tasks that are common in the field of Brain-Computer Interfaces (BCI). We investigate, on a large selection of 12 motor-imagery datasets, which ones are well suited for transfer, both as donors and as receivers. Challenges. Deep learning models typically require long training times and are data-hungry, which impedes their use for BCI systems that have to minimize the recording time for (training) examples and are subject to constraints induced by experiments involving human subjects. A solution to both issues is transfer learning, but it comes with its own challenge, i.e., substantial data distribution shifts between datasets, subjects and even between subsequent sessions of the same subject. Approach. For every pair of pre-training (donor) and test (receiver) dataset, we first train a model on the donor before training merely an additional new linear classification layer based on a few receiver trials. Performance of this transfer approach is then tested on other trials of the receiver dataset. Significance. First, we lower the threshold to use transfer learning between motor imagery datasets: the overall framework is extremely simple and nevertheless obtains decent classification scores. Second, we demonstrate that deep learning models are a good option for motor imagery cross-dataset transfer both for the reasons outlined in the first point and because the framework presented is viable in online scenarios. Finally, analysing which datasets are best suited for transfer learning can be used as a reference for future researchers to determine which to use for pre-training or benchmarking.