Labeling EEG Components with a Bag of Waveforms from Learned Dictionaries

Electroencephalograms (EEGs) are useful for analyzing brain activity, and spatiotemporal patterns in the EEG signal have clinical value, serving for example as biomarkers of diseases such as epilepsy. EEGs are a combination of components from multiple sources within the brain, the electrical activity of muscles, including the heart, and artifacts due to movement and external signals (e.g, line noise). Separating and classifying the sources of these components is important for analyzing the brain patterns in the EEG data. We propose \textit{bag-of-waves} (BoWav), a new feature for the classification of EEG independent components (ICs). BoWav represents the IC time series through the distribution of counts of waveforms from a learned shift-invariant dictionary based reconstruction. We found that BoWav has a promising predictive performance, outperforming the state-of-the-art method for IC classification, ICLabel, in two of three classes of interest.