TransformEEG: Towards improving model generalizability in deep learning-based EEG Parkinson’s disease detection
| Type of publication: | Article |
| Citation: | |
| Publication status: | Published |
| Journal: | Elsevier |
| Year: | 2025 |
| Month: | November |
| DOI: | https://doi.org/10.1016/j.neucom.2025.132075 |
| 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 of 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 (NLNSO) 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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| Added by: | [] |
| Total mark: | 0 |
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