Work

Integrating Target Domain Convex Hull with MMD for Cross-Dataset EEG Classification of Parkinson’s Disease

Year: 2025

Type: article

Abstract: Parkinson’s disease has brought great harm to human life and health. The detection of Parkinson’s disease based on electroencephalogram (EEG) provides a new way to prevent and treat Parkinson’s disease. However, due to the limited EEG data samples, there are large differences among different subjects, especially among different datasets. In this study, a new method called Improved Convex Hull and Maximum Mean Discrepancy (ICMMD)for cross-dataset classification of Parkinson’s disease is proposed by combining convex hull and transfer learning. The paper innovatively implements cross-data transfer learning in the field of brain–computer interfaces for Parkinson’s disease, using Euclidean distance for data alignment and EEG channel selection, and combines the convex envelope with MMD distance to form an effective source domain selection method. Lowpd, San and UNM datasets are used to verify the effectiveness of the proposed method through experiments on different brain regions and frequency bands in Parkinson’s. The results show that this method has good classification performance in different regions of the brain and frequency bands. The research in this paper provides a new idea and method for disease detection of Parkinson’s disease across datasets.

Source: Information

DOI: 10.3390/info17010015

Authors: Xueqi Wu, Weixiang Gao, Jiangwen Lu, Yunyuan Gao

Institution: Hangzhou Dianzi University

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,930