Detection and correction of artefacts in EEG for neurofeedback and BCI applications

Abstract

Scalp recorded electroencephalogram signals (EEG) can nowadays be recorded by pocket-size devices, giving rise to several new consumer based applications of EEG, e.g. brain computer interfaces (BCIs) and neurofeedback (NF). Using EEG ambulatory systems such as these, creates new challenges for EEG data analysis, especially concerning artifact handling. In a clinical setting, EEG can be recorded in a controlled environment, while the subject is in rest, and using additional sensors, e.g. EOG electrodes or eye tracker systems. For user-friendly applications, controlling the environment and the user, and using obstructive sensors for recording, are not possible. This requires a new perspective in the evaluation of artifact handling in EEG. In this thesis artifact handling is considered from an ambulatory-system point of view. Artifacts are classified in accordance with their impact on possible BCI and NF applications. Artifact detection performance is measured based on individual electrode signals, yielding a topographic view of artifact detectability. A method for artifact correction is chosen based on practical considerations pertaining to implementation. The influence of recording settings on correction performance is explored. A validation of artifact correction is given, based on averaged signals. Ocular artifacts have a significant impact on the analysis of EEG. The fact that they cannot be avoided, appear often (up to 15 times for eye blinks and possibly more often for movement), and have a large signal to artifact ratio (SAR)(up to -10 for frontal electrode sites and approximately 1 for the central area of the scalp), makes the correction of these artifacts necessary. The performance of three threshold-based detection methods is found to be highly subject dependent. The locations of the electrodes used for detection have a great influence on detection performance, which is closely related to SAR. For the correction of ocular artifacts from EEG recordings for BCI and NF applications, independent component analysis (ICA) is chosen as the preferable method. Key in accurate ICA artifact estimation is the location of the electrodes that are used as input. The importance of using frontal electrodes outweighs the importance of a high number of electrodes. The minimum sample rate needed for accurate ocular artifact estimation is 128 Hz. The most efficient and effective approach to correct artifacts, is to first construct a training file for each ocular artifact type. From these training files a filtering matrix is constructed, which will subsequently be used to correct artifacts as they are detected. Even under optimal conditions ICA correction is incomplete and residual artifacts remain.

Date of Award	5 Apr 2012
Original language	English
Supervisor	P.F.F. Wijn (Supervisor 1), G. Garcia Molina (External coach) & A.J.M. Denissen (External coach)