Removing Artifacts from EEG


Severe contamination of EEG activity by eye movements, blinks, muscle, heart and line noise is a serious problem for EEG interpretation and analysis. Many methods have been proposed to remove eye movement and blink artifacts from EEG recordings: A new and often preferable alternative is to apply ICA to multichannel EEG recordings and remove a wide variety of artifacts from EEG records by eliminating the contributions of artifactual sources onto the scalp sensors. Our published results show that ICA can effectively detect, separate and remove activity in EEG records from a wide variety of artifactual sources, with results comparing favorably to those obtained using regression- or PCA-based methods.

ICA Assumptions

ICA-based artifact correction can separate and remove a wide variety of artifacts from EEG data by linear decomposition. The ICA method is based on the assumptions that the time series recorded on the scalp: Assumptions two and three above are quite reasonable for EEG (or MEG) data. Given enough input data, the first assumption is reasonable as well. The method uses spatial filters derived by the ICA algorithm, and does not require a reference channel for each artifact source. Once the independent time courses of different brain and artifact sources are extracted from the data, artifact-corrected EEG signals can be derived by eliminating the contributions of the artifactual sources.


The figure below presents a schematic illustration of the procedure (Click on figure to expand). In EEG analysis, the rows of the input matrix, X, are EEG signals recorded at different electrodes and the columns are measurements recorded at different time points (left). ICA finds an `unmixing' matrix, W, which decomposes or linearly unmixes the multi-channel scalp data into a sum of temporally independent and spatially fixed components. The rows of the output data matrix, U = WX, are time courses of activation of the ICA components. The columns of the inverse matrix, inv(W), give the relative projection strengths of the respective components at each of the scalp sensors (right). These scalp weights give the scalp topography of each component, and provide evidence for the components' physiological origins. For instance:

Some Useful Heuristics