Correcting Ocular Artifacts in EEG Signals

84_neuroeletrics_WIRED_00-81 February, 2016

All of us who deal with EEG signals know how important artifacts are. EEG is one of the biological potentials with lowest amplitude (typically a few microvolts), which makes it highly sensitive to be contaminated by undesired interferences, known as artifacts. We can find several artifacts sources such as be biological (recorded activity from other sources than the brain, i.e. the heart, muscles or eye movements), electromagnetic (interferences from other electronic equipment or mains noise) and others derived from head movements or changes in the skin-electrode interface. In general we can manage artifacts in two ways: detecting their appearance and remove the entire contaminated EEG sequence or cleaning artifacts interference from the recorded EEG signal, this last technique known as artifact correction.

One of the most relevant artifacts that pollute EEG recordings comes from interferences from eyes and eyelid movements. The closer to the eyes an electrode is placed, the more it is affected by these ocular artifacts. Among EOG artifacts the most relevant ones are eye-blinks. These interferences can be in the order of hundreds of micro-volts while artifact free EEG is the order of tens of micro-volts. Ocular artifacts contaminate the EEG in the band ranging from 0 to 15 Hz interfering with three of the most commonly used EEG bands, Delta, Theta and Alpha.

There are many techniques to correct artifacts, in this post I introduce three of the most commonly used. However none of them is perfect and researchers are still devoted to find mathematical methodologies to robustly subtract these interferences from EEG recordings. To this effect, EEG set-ups usually include electrodes to record EOG signals that are usually placed above/below eyes (Vertical EOG artifacts) and next to them (Horizontal EOG artifacts). These techniques are an example of currently accepted EOG correction techniques, however, there are many more such as neural networks, wavelet analysis…

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