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...
@@ -41,13 +56,21 @@ def fetch_eegeyenet(subject="EP10", run=0, fetch_dataset_kwargs=None): """ if not fetch_dataset_kwargs: fetch_dataset_kwargs = dict() if isinstance(run, int): dataset_params = DOTS[subject][run] elif isinstance(run, str): run = [run for run in DOTS...
For some tasks we offer data from multiple paradigms. Choose the dataset used for the task, e.g. Choose the preprocessing variant, e.g. Choose data preprocessed with Hilbert transformation. Set to True for the standard ML models: config['feature_extraction'] = True ...
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...
Choose the dataset used for the task, e.g. config['dataset'] = 'antisaccade' Choose the preprocessing variant, e.g. config['preprocessing'] = 'min' Choose data preprocessed with Hilbert transformation. Set to True for the standard ML models: config['feature_extraction'] = True Include our...