TransformEEG: Towards Improving Model Generalizability in Deep Learning-based EEG Parkinson's Disease Detection

• URL: http://arxiv.org/abs/2507.07622v1
• Date: Thu, 10 Jul 2025 10:44:53 GMT
• Title: TransformEEG: Towards Improving Model Generalizability in Deep Learning-based EEG Parkinson's Disease Detection
• Authors: Federico Del Pup, Riccardo Brun, Filippo Iotti, Edoardo Paccagnella, Mattia Pezzato, Sabrina Bertozzo, Andrea Zanola, Louis Fabrice Tshimanga, Henning Müller, Manfredo Atzori,
• Abstract summary: EEG-based Deep Learning models have shown promising results.<n>Current state-of-the-art DL models suffer from poor generalizability caused by high inter-subject variability.<n>This paper introduces TransformEEG, a hybrid Convolutional-Transformer designed for Parkinson's disease detection using EEG data.
• Score: 1.2358659553221853
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Electroencephalography (EEG) is establishing itself as an important, low-cost, noninvasive diagnostic tool for the early detection of Parkinson's Disease (PD). In this context, EEG-based Deep Learning (DL) models have shown promising results due to their ability to discover highly nonlinear patterns within the signal. However, current state-of-the-art DL models suffer from poor generalizability caused by high inter-subject variability. This high variability underscores the need for enhancing model generalizability by developing new architectures better tailored to EEG data. This paper introduces TransformEEG, a hybrid Convolutional-Transformer designed for Parkinson's disease detection using EEG data. Unlike transformer models based on the EEGNet structure, TransformEEG incorporates a depthwise convolutional tokenizer. This tokenizer is specialized in generating tokens composed by channel-specific features, which enables more effective feature mixing within the self-attention layers of the transformer encoder. To evaluate the proposed model, four public datasets comprising 290 subjects (140 PD patients, 150 healthy controls) were harmonized and aggregated. A 10-outer, 10-inner Nested-Leave-N-Subjects-Out (N-LNSO) cross-validation was performed to provide an unbiased comparison against seven other consolidated EEG deep learning models. TransformEEG achieved the highest balanced accuracy's median (78.45%) as well as the lowest interquartile range (6.37%) across all the N-LNSO partitions. When combined with data augmentation and threshold correction, median accuracy increased to 80.10%, with an interquartile range of 5.74%. In conclusion, TransformEEG produces more consistent and less skewed results. It demonstrates a substantial reduction in variability and more reliable PD detection using EEG data compared to the other investigated models.

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