Blind Source Separation Techniques

A dynamic generative model can extract interpretable oscillatory components from multichannel neurophysiological recordings

This paper introduces a dynamic generative model to extract interpretable oscillatory components from multichannel neurophysiological recordings, improving component selection and interpretation.

Narrowband multivariate source separation for semi-blind discovery of experiment contrasts

This paper introduces csGED, a method for separating neural signals in MEG data by combining frequency and condition-specific analysis, showing improved performance over existing methods.

Multi-context blind source separation by error-gated Hebbian rule

This paper demonstrates that a neural network using the error-gated Hebbian rule can learn to perform multi-context blind source separation by retaining context memories and extracting common features.

Multi-band component analysis for EEG artifact removal and source reconstruction with application to gamma-band activity

This paper introduces multi-band ICA to improve EEG artifact removal and source reconstruction by increasing component count and SNR, outperforming traditional wide-band ICA.