EEGEyeNet is a benchmark to evaluate ET prediction based on EEG measurements with an increasing level of difficulty. Overview The repository consists of general functionality to run the benchmark and custom implementation of different machine learning models. We offer to run standard ML models (e....
The EEGEyeNet dataset merges EEG data with eye-tracking technology to advance cognitive research at the intersection of brain dynamics and eye movement. By developing machine learning models to predict eye movements from EEG data, we gain insights into perceptual, attentional, and cognitive processes...
dataset_name="EEGEYENET", ) ] ), } DOTS = {"EP10": PARAMS["EP10_DOTS"]} def _get_urls_df(): return pd.read_csv(Path(__file__).parent / "eegeyenet_urls.csv") def _get_params(subject, run): df = _get_urls_df() row = df.loc[(df.subject == subject.upper()) & (df...
conda create -n eegeyenet_benchmark python=3.8.5 First install the general_requirements.txt conda install --file general_requirements.txt Pytorch Requirements If you want to run the pytorch DL models, first install pytorch in the recommended way. For Linux users with GPU support this is: ...
In this paper, we use emerging Riemannian geometry based classifiers and regressors to perform eye-tracking tasks over a 2021 dataset: EEGEyeNet. The classification task we attempt is determining Left/Right eye movement, and the regression task we attempt is determining absolute eye position on a...
However, most existing methods for fusing EEG and eye movement signals use concatenation or weighted summation, which may lead to information loss and limited ability to resist noise. To tackle this issue, in this paper, we propose a Coordinated-representation Decision Fusion Network (CoDF-Net) ...