Analysis of low-noise EEG in search of functional gamma band correlates

• Main Author: Hamzei, Nazanin
• Language: English
• Published: University of British Columbia 2017
• Online Access: http://hdl.handle.net/2429/61337

The electroencephalogram (EEG) has proven to be a useful information source in analysis of brain activity, diagnosis of neurological disorders, and development of brain-computer interfaces (BCI’s). Through numerous studies over the past decades, EEG activity in different frequency bands has been observed to correspond with various mental states. Clinical use of EEG, however, is often limited to frequency ranges below 30 Hz, ignoring potentially informative patterns within the gamma band (30-100 Hz). Indeed, the gamma band has received greater scrutiny in recent years and is typically known to underlie and be modulated by sensorimotor behaviors and internal cognitive processes. In this study, we have investigated the potential of an ultra-low noise capsule at the LSBB (Laboratoire Souterrain a Bas Bruit, Rustrel, France) for acquisition of clean EEG signals, with a focus on analysis of high frequencies (gamma band) in search for novel activity patterns. Using a battery-operated EEG acquisition system, we acquired 64-channel EEG recordings from a few volunteers performing several cognitive, sensory, and motor tasks in both LSBB and a typical research laboratory. Upon analysis of this data using Stockwell Transform, we compared task-specific gamma band energy increases of signals acquired at the two environments, observing more prominent functional EEG changes in LSBB. Moreover, we studied all recordings in both environments to examine statistically significant spatial and spectral correlates of spontaneous EEG pertaining to each of the tasks. To further assess the task-induced changes in the EEG signals, we have also proposed a framework for analyzing gamma band connectivity; i.e. functional patterns of interaction between distinct channels of the EEG. Using this framework, we have analyzed directional connectivity on recordings pertaining to motor tasks, both in a batch-based (yielding a time-averaged pattern) and an instantaneous manner. Batch-based connectivity analysis of the data resulted in well-defined connectivity patterns among subjects, while instantaneous connectivity analysis was inconsistent due to limitations of the study protocol. The results obtained in this thesis demonstrate the potential of the low-noise capsule and motivate further protocol enhancements and analysis methods for conducting high-frequency EEG studies at LSBB. === Applied Science, Faculty of === Electrical and Computer Engineering, Department of === Graduate