InfoFlowNet: A Multi-head Attention-based Self-supervised Learning Model with Surrogate Approach for Uncovering Brain Effective Connectivity

no code implementations • 30 Nov 2023 • Chun-Hsiang Chuang, Shao-Xun Fang, Chih-Sheng Huang, Weiping Ding

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.

Causal Discovery Causal Inference +3

IC-U-Net: A U-Net-based Denoising Autoencoder Using Mixtures of Independent Components for Automatic EEG Artifact Removal

1 code implementation • 19 Nov 2021 • Chun-Hsiang Chuang, Kong-Yi Chang, Chih-Sheng Huang, Tzyy-Ping Jung

Electroencephalography (EEG) signals are often contaminated with artifacts.

Denoising EEG +1

MS-DARTS: Mean-Shift Based Differentiable Architecture Search

1 code implementation • 23 Aug 2021 • Jun-Wei Hsieh, Ming-Ching Chang, Ping-Yang Chen, Santanu Santra, Cheng-Han Chou, Chih-Sheng Huang

Our approach can improve bot the stability and accuracy of DARTS, by smoothing the loss landscape and sampling architecture parameters within a suitable bandwidth.