Advancing EEG-Based Gaze Prediction Using Depthwise Separable Convolution and Enhanced Pre-Processing
This work addresses EEG-based gaze prediction for applications like assistive technologies, but it is incremental as it builds on existing vision transformer and pre-processing methods.
The study tackled EEG-based gaze prediction by developing the EEG-Deeper Clustered Vision Transformer (EEG-DCViT), which combines depthwise separable CNNs with vision transformers and enhanced pre-processing, achieving a new benchmark with an RMSE of 51.6 mm.
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.