Abstract
Electroencephalography (EEG) signals are frequently contaminated with unwanted electrooculographic (EOG) artifacts. Blinks and eye movements generate large amplitude peaks that corrupt EEG measurements. Independent component analysis (ICA) has been used extensively in manual and automatic methods to remove artifacts. By decomposing the signals into neural and artifactual components and artifact components can be eliminated before signal reconstruction. Unfortunately, removing entire components may result in losing important neural information present in the component and eventually may distort the spectral characteristics of the reconstructed signals. An alternative approach is to correct artifacts within the independent components instead of rejecting the entire component, for which wavelet transform based decomposition methods have been used with good results. An improved, fully automatic wavelet-based component correction method is presented for EOG artifact removal that corrects EOG components selectively, i.e., within EOG activity regions only, leaving other parts of the component untouched. In addition, the method does not rely on reference EOG channels. The results show that the proposed method outperforms other component rejection and wavelet-based EOG removal methods in its accuracy both in the time and the spectral domain. The proposed new method represents an important step towards the development of accurate, reliable and automatic EOG artifact removal methods.
Highlights
Electroencephalography (EEG) is a non-invasive method for measuring brain activity
The proposed method (PM) is compared to the traditional full component rejection method (ICArej) [58] and the wavelet-enhanced Independent component analysis (ICA) [6] component correction methods using the performance metrics specified in Section 3.4. wICA is compared to rejection
With respect to the ∆SNR metric, the wICA method was significantly better than the reject ICA method (a)
Summary
Electroencephalography (EEG) is a non-invasive method for measuring brain activity. Due to its low cost and high temporal resolution, it is routinely used in clinical diagnostics, epilepsy surgery and cognitive psychology research. A major concern when processing EEG measurement data is the presence of various artifacts that are generated by extra-cerebral sources, such as eye blinks and eye movements (electrooculographic/EOG artifacts), muscle movement (neck, jaw and face muscles; electromyogram/EMG artifact) or heart-related EEG disturbances (electrocardiography/ECG artifact and pulse artifact). These artifacts distort the measured EEG signals and, in the worst case, can make entire measurement datasets unusable. Besides being a very labor-intensive task that requires a trained expert, this method cannot be automated
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