%Aigaion2 BibTeX export from HES SO Valais Publications
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@ARTICLE{,
    author = {M{\"{u}}ller, Henning and Atzori, Manfredo and Del Pup, Federico and Brun, Riccardo and Iotti, Filippo and Paccagnella, Edoardo and Pezzato, Mattia and Bertozzo, Sabrina and Zanola, Andrea and Tshimanga, Louis Fabrice},
     month = nov,
     title = {TransformEEG: Towards improving model generalizability in deep learning-based EEG Parkinson’s disease detection},
   journal = {Elsevier},
      year = {2025},
       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.}
}