Publication

OCULAR ARTIFACT REMOVAL FROM EEG: A COMPARISON OF SUBSPACE PROJECTION AND ADAPTIVE FILTERING METHODS

Touradj Ebrahimi, Jean-Marc Vesin, Ashkan Yazdani, Eleni Kroupi
2011
Conference Papers

Abstract

One of the fundamental challenges in EEG signal processing is the selection of a proper method to correct ocular artifacts in the recorded electroencephalogram (EEG). Several methods have been proposed for this task. Among these methods, two main categories, namely subspace projection and adaptive filtering, have gained more popularity and are widely used in EEG processing applications. The main objective of this paper is to perform a comparative study of the performances of these methods using two measures, namely the mean square error (MSE) and the computational time of each algorithm. According to this study, ICA (independent component analysis) methods appear to be the most robust but not the fastest ones. Hence, they could be easily used for off-line applications. Moreover, PCA (principal component analysis) is very fast, but less accurate, so it could be used for real-time applications. Finally, adaptive filtering appears to have the worst performance in terms of accuracy, but it is very fast. Therefore, it could be also used for real-time applications, in which speed matters more than accuracy.

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Touradj Ebrahimi, Jean-Marc Vesin, Ashkan Yazdani, Eleni Kroupi
2011
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/2f3c5086-e570-4694-a69f-1f10736ee6d7

About this result

Related concepts (32)

Electroencephalography

Electroencephalography (EEG) is a method to record an electrogram of the spontaneous electrical activity of the brain. The biosignals detected by EEG have been shown to represent the postsynaptic potentials of pyramidal neurons in the neocortex and allocortex. It is typically non-invasive, with the EEG electrodes placed along the scalp (commonly called "scalp EEG") using the International 10–20 system, or variations of it. Electrocorticography, involving surgical placement of electrodes, is sometimes called "intracranial EEG".

Principal component analysis

Principal component analysis (PCA) is a popular technique for analyzing large datasets containing a high number of dimensions/features per observation, increasing the interpretability of data while preserving the maximum amount of information, and enabling the visualization of multidimensional data. Formally, PCA is a statistical technique for reducing the dimensionality of a dataset. This is accomplished by linearly transforming the data into a new coordinate system where (most of) the variation in the data can be described with fewer dimensions than the initial data.

Mean squared error

In statistics, the mean squared error (MSE) or mean squared deviation (MSD) of an estimator (of a procedure for estimating an unobserved quantity) measures the average of the squares of the errors—that is, the average squared difference between the estimated values and the actual value. MSE is a risk function, corresponding to the expected value of the squared error loss. The fact that MSE is almost always strictly positive (and not zero) is because of randomness or because the estimator does not account for information that could produce a more accurate estimate.

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