# Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline

arXiv: https://arxiv.org/abs/2007.03746 · HTML: https://mlsanity.com/paper/2007.03746/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2020.

## Built on by (most cited first, 30 of 31)

- [Visual Decoding and Reconstruction via EEG Embeddings with Guided Diffusion](https://mlsanity.com/paper/2403.07721/) (2024)
- [T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs](https://mlsanity.com/paper/2412.07228/) (2024)
- [A Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding](https://mlsanity.com/paper/2401.10746/) (2024)
- [Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment](https://mlsanity.com/paper/2412.17842/) (2024)
- [DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding](https://mlsanity.com/paper/2506.21140/) (2025)
- [Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review](https://mlsanity.com/paper/2102.02729/) (2021)
- [Evaluating the structure of cognitive tasks with transfer learning](https://mlsanity.com/paper/2308.02408/) (2023)
- [Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces](https://mlsanity.com/paper/2412.01079/) (2024)
- [Revisiting Euclidean Alignment for Transfer Learning in EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2502.09203/) (2025)
- [Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification](https://mlsanity.com/paper/2503.05349/) (2025)
- [Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs](https://mlsanity.com/paper/2411.02094/) (2024)
- [User-wise Perturbations for User Identity Protection in EEG-Based BCIs](https://mlsanity.com/paper/2411.10469/) (2024)
- [Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2412.09006/) (2024)
- [MIRepNet: A Pipeline and Foundation Model for EEG-Based Motor Imagery Classification](https://mlsanity.com/paper/2507.20254/) (2025)
- [Protecting Multiple Types of Privacy Simultaneously in EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/2411.19498/) (2024)
- [Adversarial Filtering Based Evasion and Backdoor Attacks to EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2412.07231/) (2024)
- [PAT: Privacy-Preserving Adversarial Transfer for Accurate, Robust and Privacy-Preserving EEG Decoding](https://mlsanity.com/paper/2412.11390/) (2024)
- [MVCNet: Multi-View Contrastive Network for Motor Imagery Classification](https://mlsanity.com/paper/2502.17482/) (2025)
- [Channel Reflection: Knowledge-Driven Data Augmentation for EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2412.03224/) (2024)
- [SAFE: Secure and Accurate Federated Learning for Privacy-Preserving Brain-Computer Interfaces](https://mlsanity.com/paper/2601.05789/) (2026)
- [Tailoring deep learning for real-time brain-computer interfaces: From offline models to calibration-free online decoding](https://mlsanity.com/paper/2507.06779/) (2025)
- [STAMBRIDGE: Spectral-Temporal Amplitude-aware Mid-Feature Bridge for EEG Visual Decoding](https://mlsanity.com/paper/2605.23137/) (2026)
- [End-to-End Deep Transfer Learning for Calibration-free Motor Imagery Brain Computer Interfaces](https://mlsanity.com/paper/2307.12827/) (2023)
- [A Dynamic Domain Adaptation Deep Learning Network for EEG-based Motor Imagery Classification](https://mlsanity.com/paper/2309.11714/) (2023)
- [From Electrode to Global Brain: Integrating Multi- and Cross-Scale Brain Connections and Interactions Under Cross-Subject and Within-Subject Scenarios](https://mlsanity.com/paper/2411.05862/) (2024)
- [Spiking Neural Network for Intra-cortical Brain Signal Decoding](https://mlsanity.com/paper/2504.09213/) (2025)
- [AFPM: Alignment-based Frame Patch Modeling for Cross-Dataset EEG Decoding](https://mlsanity.com/paper/2507.11911/) (2025)
- [DAMSDAN: Distribution-Aware Multi-Source Domain Adaptation Network for Cross-Domain EEG-based Emotion Recognition](https://mlsanity.com/paper/2510.17475/) (2025)
- [Backpropagation-Free Test-Time Adaptation for Lightweight EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2601.07556/) (2026)
- [Transformer Based Model for Spatiotemporal Feature Learning in EEG Emotion Recognition](https://mlsanity.com/paper/2606.10718/) (2026)

## Abstract (arXiv)

Transfer learning (TL) has been widely used in motor imagery (MI) based brain-computer interfaces (BCIs) to reduce the calibration effort for a new subject, and demonstrated promising performance. While a closed-loop MI-based BCI system, after electroencephalogram (EEG) signal acquisition and temporal filtering, includes spatial filtering, feature engineering, and classification blocks before sending out the control signal to an external device, previous approaches only considered TL in one or two such components. This paper proposes that TL could be considered in all three components (spatial filtering, feature engineering, and classification) of MI-based BCIs. Furthermore, it is also very important to specifically add a data alignment component before spatial filtering to make the data from different subjects more consistent, and hence to facilitate subsequential TL. Offline calibration experiments on two MI datasets verified our proposal. Especially, integrating data alignment and sophisticated TL approaches can significantly improve the classification performance, and hence greatly reduces the calibration effort.