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Contrastive Learning of Shared Spatiotemporal EEG Representations Across Individuals for Naturalistic Neuroscience
Targeting the Electroencephalogram (EEG) technique, known for its rich spatial and temporal information, this study presents a general framework for Contrastive Learning of Shared SpatioTemporal EEG Representations across individuals (CL-SSTER).
Perturbing a Neural Network to Infer Effective Connectivity: Evidence from Synthetic EEG Data
Specifically, we trained neural networks (i. e., CNN, vanilla RNN, GRU, LSTM, and Transformer) to predict future EEG signals according to historical data and perturbed the networks' input to obtain effective connectivity (EC) between the perturbed EEG channel and the rest of the channels.
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition
Contrastive learning was employed to minimize the inter-subject differences by maximizing the similarity in EEG signal representations across subjects when they received the same emotional stimuli in contrast to different ones.
