Advancing EEG-Based Gaze Prediction Using Depthwise Separable Convolution and Enhanced Pre-Processing
<|reference_start|>Advancing EEG-Based Gaze Prediction Using Depthwise Separable Convolution and Enhanced Pre-Processing: In the field of EEG-based gaze prediction, the application of deep learning to interpret complex neural data poses significant challenges. This study evaluates the effectiveness of pre-processing techniques and the effect of additional depthwise separable convolution on EEG vision transformers (ViTs) in a pretrained model architecture. We introduce a novel method, the EEG Deeper Clustered Vision Transformer (EEG-DCViT), which combines depthwise separable convolutional neural networks (CNNs) with vision transformers, enriched by a pre-processing strategy involving data clustering. The new approach demonstrates superior performance, establishing a new benchmark with a Root Mean Square Error (RMSE) of 51.6 mm. This achievement underscores the impact of pre-processing and model refinement in enhancing EEG-based applications.<|reference_end|>
@article{key2024advancing,
    title={Advancing EEG-Based Gaze Prediction Using Depthwise Separable
  Convolution and Enhanced Pre-Processing},
    author={Matthew L Key, Tural Mehtiyev, Xiaodong Qu},
    journal={International Conference on Human-Computer Interaction (HCII 2024)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03480},
    primaryClass={cs.LG}
}