SVM for Semi-automatic Selection of ICA Components of Electromyogenic Artifacts in EEG Data

Abstract

Traditionally, electroencephalography (EEG) recorded during movement has been considered too noise prone to allow for sophisticated analysis. Superimposed electromyogenic activity interferes and masks the EEG signal. Presently, computational techniques such as Independent Component Analysis allow reduction of these artifacts. However, to date, it is relied on the user to select the artifact-contaminated components to reject.

To automate this process and to reduce user dependent factors, we trained a support vector machine (SVM) to assist the user in choosing the independent components (ICs) most influenced by electromyogenic artifacts. We designed and conducted a study with specific neck and body movement exercises and collected data from five human participants (35 datasets total). After preprocessing, we decomposed the data by applying the Adaptive Mixture of Independent Component Analysis (AMICA) algorithm. An expert labeled the ICs found in the EEG recordings after decomposition as either ‘myogenic activity’ or ‘non-myogenic activity’.

Afterwards, the classifier was evaluated on the dataset of one participant, whose data were not used in the training phase, and obtained 93% sensitivity and 96% specificity.

Our study was designed to cover a diverse selection of exercises that stimulate the musculature that most interferes in EEG recordings during movement. This selection should produce similar artifact patterns as seen in most exercises or movements. Although unfamiliar exercises could result in worse classification performance, the results are expected to be equivalent to ours.

Our study showed that this tool can help EEG analysis by reliably and efficiently choosing electromyogenic artifact contaminated components after AMICA decomposition, ultimately increasing the speed of data processing.