# A swap-adversarial framework for domain generalization in pre- and post-stimulation Parkinsonian state classification

DevFeed: [A swap-adversarial framework for domain generalization in pre- and post-stimulation Parkinsonian state classification](<https://devfeed.tech/articles/a-swap-adversarial-framework-for-domain-generalization-in-pre-and-post-stimulation-parkinsonian-state-classification-102935.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2602.10528>)

Author: Seongwon Jin, Hanseul Choi, Sunggu Yang, Sungho Park, Jibum Kim

Published: 2026-10-28T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Artificial Intelligence](<https://devfeed.tech/sources/arxiv-artificial-intelligence.md>), [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [bci](<https://devfeed.tech/topics/bci.md>), [Dataset development](<https://devfeed.tech/topics/dataset-development.md>)

Tags: [2](<https://devfeed.tech/tags/2.md>), [3](<https://devfeed.tech/tags/3.md>), [adversarial](<https://devfeed.tech/tags/adversarial.md>), [arxiv](<https://devfeed.tech/tags/arxiv.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [brain](<https://devfeed.tech/tags/brain.md>), [classification](<https://devfeed.tech/tags/classification.md>), [cross](<https://devfeed.tech/tags/cross.md>), [cs-ai](<https://devfeed.tech/tags/cs-ai.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [data](<https://devfeed.tech/tags/data.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [domain](<https://devfeed.tech/tags/domain.md>), [eeg](<https://devfeed.tech/tags/eeg.md>), [effectiveness](<https://devfeed.tech/tags/effectiveness.md>), [experiments](<https://devfeed.tech/tags/experiments.md>), [features](<https://devfeed.tech/tags/features.md>), [interface](<https://devfeed.tech/tags/interface.md>), [learning](<https://devfeed.tech/tags/learning.md>), [model](<https://devfeed.tech/tags/model.md>), [neural](<https://devfeed.tech/tags/neural.md>)

## AI overview

The framework aims to improve Parkinsonian state classification across subjects and recording domains by combining robust preprocessing, inter-subject balanced channel swap (ISBCS), and domain-adversarial learning (DAL). ISBCS augments data by swapping functionally corresponding channels across subjects according to a brain map; DAL is used to reduce subject-specific bias and promote shared task-relevant features. The authors also introduce a reproducible ECoG benchmark from long-term recordings in 6-hydroxydopamine-induced rat models, annotated before and after electrical stimulation. They report robust generalization on the new ECoG and public EEG datasets across cross-subject, cross-session, and cross-dataset experiments. The supplied text does not give quantitative results or study limitations.

## Source excerpt

arXiv:2602.10528v3 Announce Type: replace-cross Abstract: 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. 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. We validate the effectiveness of this framework through extensive experiments under cross-subject, cross-session, and cross-dataset settings. Our results show that the proposed framework exhibits the best and robust generalization performance both for our newly constructed ECoG and public EEG datasets.