# A swap-adversarial framework for improving domain generalization in electrocorticography-based Parkinson's disease classification

arXiv: https://arxiv.org/abs/2602.10528 · HTML: https://mlsanity.com/paper/2602.10528/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 67 references that are in the map.
- First posted on arXiv 11 Feb 2026.

## Builds on (most cited first, 22 of 22)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization](https://mlsanity.com/paper/1911.08731/) (2019)
- [Domain Generalization via Invariant Feature Representation](https://mlsanity.com/paper/1301.2115/) (2013)
- [Generalizing Across Domains via Cross-Gradient Training](https://mlsanity.com/paper/1804.10745/) (2018)
- [Learning Not to Learn: Training Deep Neural Networks with Biased Data](https://mlsanity.com/paper/1812.10352/) (2018)
- [EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces](https://mlsanity.com/paper/2006.00622/) (2020)
- [CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals](https://mlsanity.com/paper/2106.13695/) (2021)
- [MADG: Margin-based Adversarial Learning for Domain Generalization](https://mlsanity.com/paper/2311.08503/) (2023)
- [Learning from Heterogeneous EEG Signals with Differentiable Channel Reordering](https://mlsanity.com/paper/2010.13694/) (2020)
- [DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding](https://mlsanity.com/paper/2506.21140/) (2025)
- [EEG-DG: A Multi-Source Domain Generalization Framework for Motor Imagery EEG Classification](https://mlsanity.com/paper/2311.05415/) (2023)
- [Towards Best Practice of Interpreting Deep Learning Models for EEG-based Brain Computer Interfaces](https://mlsanity.com/paper/2202.06948/) (2022)
- [Towards Stable and Comprehensive Domain Alignment: Max-Margin Domain-Adversarial Training](https://mlsanity.com/paper/2003.13249/) (2020)
- [Impact of dataset size and long-term ECoG-based BCI usage on deep learning decoders performance](https://mlsanity.com/paper/2209.03789/) (2022)
- [Parkinson's Disease Detection from Resting State EEG using Multi-Head Graph Structure Learning with Gradient Weighted Graph Attention Explanations](https://mlsanity.com/paper/2408.00906/) (2024)
- [RISE-iEEG: Robust to Inter-Subject Electrodes Implantation Variability iEEG Classifier](https://mlsanity.com/paper/2408.14477/) (2024)
- [SDC-Net: A Domain Adaptation Framework with Semantic-Dynamic Consistency for Cross-Subject EEG Emotion Recognition](https://mlsanity.com/paper/2507.17524/) (2025)
- [Bi-Band ECoGNet for ECoG Decoding on Classification Task](https://mlsanity.com/paper/2412.00378/) (2024)
- [Explainable AI-Driven Neural Activity Analysis in Parkinsonian Rats under Electrical Stimulation](https://mlsanity.com/paper/2502.12471/) (2025)

## Abstract (arXiv)

We propose a novel swap-adversarial framework that mitigates high inter-subject variability and the high-dimensional low-sample-size problem in electrocorticography (ECoG) data. It achieves robust domain generalization across ECoG and electroencephalography (EEG)-based brain-computer interface datasets. Our framework integrates (1) robust preprocessing, (2) inter-subject balanced channel swap (ISBCS) for cross-subject augmentation, and (3) domain-adversarial learning (DAL) to suppress subject-specific bias. The ISBCS method is a bio-inspired channel swapping strategy that exchanges only functionally corresponding channels across subjects, guided by a brain map, to mitigate inter-subject distribution differences. The DAL strategy encourages the model to learn task-relevant shared features. We validate the effectiveness of this framework through extensive experiments under cross-subject, cross-session, and cross-dataset settings. Our framework consistently outperforms all baselines across all settings, showing the most significant improvements in highly variable environments. It also achieves superior cross-dataset performance between public EEG benchmarks, demonstrating strong generalization capability not only for ECoG but also for EEG data. In addition, we introduce a new ECoG dataset, the first reproducible benchmark, which is constructed from long-term ECoG recordings of 6-hydroxydopamine-induced rat models and annotated with neural responses measured before and after electrical stimulation.