Master Thesis Defense - March 20, 2017: Artifact Analysis and Removal of Electroencephalographic (EEG) Recordings

Yi Dou

Monday, March 20, 2017 at 2:00 p.m.
Room EV001.162

You are invited to attend the following M.A.Sc. (Quality Systems Engineering) thesis examination.

Examining Committee

Dr. J.Y. Yu, Chair
Dr. Y. Zeng, Supervisor
Dr. W. Zhu, Supervisor
Dr. J.Y. Yu, CIISE Examiner
Dr. Z. Chen, External Examiner (BCEE)

Abstract

Electroencephalography (EEG) technique has been widely used in continuous monitoring the brain activities in research studies and clinical applications. In cognitive neuroscience research, the electrical brain signals can be used to measure mental effort of subjects. However, the presence of artifacts is a constant problem when recording brain activities, which will obscure the underlying neural dynamics and therefore make it more complex to interpret EEG signals. These unwanted signals have different effects depending on the sources of the artifacts. Among them, the motion of the subject is one of the major contributors to physiological artifacts that causes most of the contaminations to the underlying brain activities. It is quite challenging to correct myogenic activity from EEG background potentials due to its wide spectral distribution overlapped with typical bands of brain waves related to cognitive activities, and the spatial distribution over the entire scalp of human. Thus, we focus on the analysis and removal of motion artifact from EEG signals.

The preliminary investigations include the movement-triggered artifact identification and the analysis of the characteristics of motion artifact. According to the recorded video, the contaminated epochs are extracted from the continuous EEG signals. A set of features of movement-triggered artifacts are proposed based on power spectral density and wavelet transform. Statistical analysis is performed to distinguish the segments that contain motions. After that, two typical methods of arti- fact removal are applied, and the efficiency to correct this type of artifact is validated by comparing the extracted features of non-movement segments and contaminated segments. The result shows that the tested artifact removal methods cannot completely remove movement artifacts, which also infers the potential relation between motion and mental activities.