Detection of Depression in EEG Signals Based on Convolutional Transformer and Adaptive Transfer Learning

EasyChair Preprint 13754

Abstract

Electroencephalography(EEG) signals provide an objective reflection of the inner workings of the brain, making them a promising tool for the diagnosis of depression. However, the classification of EEG signals for depression is severely affected by individual differences among subjects, complex intrinsic properties, and low Signal-to-Noise Ratio(SNR), which limits the classification accuracy. Additionally, traditional convolutional neural networks extract local features but fail to capture long-term dependencies in EEG decoding. To address the aforementioned issues, we introduce an adaptive transfer learning method based on a convolutional transformer model for depression detection. The experimental results demonstrate the effectiveness of the proposed model on the public MODMA dataset and EDRA dataset. The results indicate that the MODMA and EDRA datasets exhibit optimal accuracies of 100% and 98.61%, respectively, outperforming some state-of-the-art depression identification methods. Our findings provide new perspectives on the recognition of depression, which could be used as an assisted diagnostic tool in the future.

Keyphrases: Convolutional transformer, Depression Detection, EEG, Transfer Learning

@booklet{EasyChair:13754,
  author    = {Qianqian Tan and Minmin Miao},
  title     = {Detection of Depression in EEG Signals Based on Convolutional Transformer and Adaptive Transfer Learning},
  howpublished = {EasyChair Preprint 13754},
  year      = {EasyChair, 2024}}