Search Results for author:
Found 3 papers, 1 papers with code
InfoFlowNet: A Multi-head Attention-based Self-supervised Learning Model with Surrogate Approach for Uncovering Brain Effective Connectivity
In this study, we introduce a novel brain causal inference model named InfoFlowNet, which leverages the self-attention mechanism to capture associations among electroencephalogram (EEG) time series.
Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)
Electroencephalogram (EEG) signals are frequently used in brain-computer interfaces (BCIs), but they are easily contaminated by artifacts and noises, so preprocessing must be done before they are fed into a machine learning algorithm for classification or regression.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
