| Autor: |
Key, Matthew L, Mehtiyev, Tural, Qu, Xiaodong |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
International Conference on Human-Computer Interaction (HCII 2024) |
| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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