DECAN: A Denoising Encoder via Contrastive Alignment Network for Dry Electrode EEG Emotion Recognition
<|reference_start|>DECAN: A Denoising Encoder via Contrastive Alignment Network for Dry Electrode EEG Emotion Recognition: EEG signal is important for brain-computer interfaces (BCI). Nevertheless, existing dry and wet electrodes are difficult to balance between high signal-to-noise ratio and portability in EEG recording, which limits the practical use of BCI. In this study, we propose a Denoising Encoder via Contrastive Alignment Network (DECAN) for dry electrode EEG, under the assumption of the EEG representation consistency between wet and dry electrodes during the same task. Specifically, DECAN employs two parameter-sharing deep neural networks to extract task-relevant representations of dry and wet electrode signals, and then integrates a representation-consistent contrastive loss to minimize the distance between representations from the same timestamp and category but different devices. To assess the feasibility of our approach, we construct an emotion dataset consisting of paired dry and wet electrode EEG signals from 16 subjects with 5 emotions, named PaDWEED. Results on PaDWEED show that DECAN achieves an average accuracy increase of 6.94$\%$ comparing to state-of-the art performance in emotion recognition of dry electrodes. Ablation studies demonstrate a decrease in inter-class aliasing along with noteworthy accuracy enhancements in the delta and beta frequency bands. Moreover, an inter-subject feature alignment can obtain an accuracy improvement of 5.99$\%$ and 5.14$\%$ in intra- and inter-dataset scenarios, respectively. Our proposed method may open up new avenues for BCI with dry electrodes. PaDWEED dataset used in this study is freely available at https://huggingface.co/datasets/peiyu999/PaDWEED.<|reference_end|>
@article{zhang2024decan:,
    title={DECAN: A Denoising Encoder via Contrastive Alignment Network for Dry
  Electrode EEG Emotion Recognition},
    author={Meihong Zhang, Shaokai Zhao, Shuai Wang, Zhiguo Luo, Liang Xie, Tiejun
  Liu, Dezhong Yao, Ye Yan and Erwei Yin},
    journal={arXiv preprint arXiv:2409.03976},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.03976},
    primaryClass={cs.HC}
}