arXiv:2408.03478
공식 코드 · Papers with Code
Effect of Kernel Size on CNN-Vision-Transformer-Based Gaze Prediction Using Electroencephalography Data
원문 초록
In this paper, we present an algorithm of gaze prediction from Electroencephalography (EEG) data. EEG-based gaze prediction is a new research topic that can serve as an alternative to traditional video-based eye-tracking. Compared to the existing state-of-the-art (SOTA) method, we improved the root mean-squared-error of EEG-based gaze prediction to 53.06 millimeters, while reducing the training time to less than 33% of its original duration. Our source code can be found at https://github.com/AmCh-Q/CSCI6907Project
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Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins
eegFloss: A Python package for refining sleep EEG recordings using machine learning models
Cautious Next Token Prediction
Where, What, Why: Towards Explainable Driver Attention Prediction
DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding
Out-of-Distribution Semantic Occupancy Prediction
토론
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