Detection and classification of subject-generated artifacts in EEG signals using autoregressive models

Abstract

We examine the problem of accurate detection and classification of artifacts in continuous EEG recordings. Manual identification of artifacts, by means of an expert or panel of experts, can be tedious, time-consuming and infeasible for large datasets. We use autoregressive (AR) models for feature extraction and characterization of EEG signals containing several kinds of subject-generated artifacts. AR model parameters are scale-invariant features that can be used to develop models of artifacts across a population. We use a support vector machine (SVM) classifier to discriminate among artifact conditions using the AR model parameters as features. Results indicate reliable classification among several different artifact conditions across subjects (approximately 94%). These results suggest that AR modeling can be a useful tool for discriminating among artifact signals both within and across individuals.

Information Theoretic Approaches for Motor-Imagery BCI Systems: Review and Experimental Comparison

“…The selected frequency bands correspond to the brainwaves theta (4-7 Hz), alpha (8-15 Hz), beta (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31) and low gamma (32)(33)(34)(35)(36)(37)(38)(39)(40), where five-fold cross-validation has been used to select the best combination of these frequency bands. We extract d features from each band, where d is selected using the method in [72].…”

“…In the rest of the paper, it is assumed that the signals have already been pre-processed to remove noise and interferences. To this end, several techniques [25], such as autoregressive modeling [26], the more complex independent component analysis (ICA) [27], or the signal space projection (SSP) method [28], have shown good or excellent results (see also [29] and the references therein). Signal preprocessing includes also the division of the EEG into several frequency bands that are separately analyzed [30,31].…”

“…Signal preprocessing includes also the division of the EEG into several frequency bands that are separately analyzed [30,31]. The "mu" band (8-15 Hz) and the "beta" band (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31) are particularly useful in BCIs, as they originate from the sensorimotor cortex, i.e., the area that controls voluntary movements [2].…”

Extracting features from phase space of EEG signals in brain–computer interfaces

“…Autoregressive (AR) methods have been used in a number of studies to model EEG data by representing the signal at each channel as a linear combination of the signal at previous time points [30]. AR models provide a compact, computationally efficient representation of EEG signals.…”

Eye Blink Artifact Removal in EEG Using Tensor Decomposition

“…There are techniques, which are based in thresholds applied in time or frequency domain like in [3]. Some others are based in a supervised learning method using statistical [4] or autoregressive (AR) [5] features. Other methods, like [6], use a combination of feature extraction and data driven thresholds.…”