EEG OSCILLATORY ACTIVITIES FROM HUMAN MOTOR BRAIN

Abstract

Motor skills are essential in people’s daily life in exploring and interacting with the ambient environment. Impairments to motor functions affect the acquisition of motor skills, which not only reduce the quality of life, but also impose heavy economic burdens to sufferers and their families. Oscillatory activities in electroencephalography (EEG), such as the mu rhythm, present functional correlation to motor functions, which provide accessible windows to understand underlying neural mechanism in healthy persons and perform diagnoses in patients with various motor impairments. It is thus of significant importance to further investigate classic and/or identify new motor-related EEG oscillatory activities. In this dissertation, EEG oscillations from both infants and adults are investigated to uncover motor-related neural information noninvasively from the human brain regarding their developmental changes and movement representations of body parts, respectively. In typical developing infants at 5-7 months of age, knowledge about mu rhythm development is expanded by capturing subtle developmental changes of its characteristics in a fine age resolution, through the development of new spatio-spectral analysis of EEG data recorded longitudinally on a weekly basis. In adults, motor tasks involving fine body parts are studied to investigate EEG resolutions in decoding movements/motor imageries of individual fingers, which have only been addressed in large body parts in literature. Discriminative information in EEG oscillations about motor tasks of fine body parts is revealed through the discovery of a novel type of spectral structures in EEG, which exhibits better sensitivity to movements of fine body parts than the classic mu rhythm. The findings in this dissertation broaden the scope of neural information in EEG oscillations in relation to motor functions, and contribute to the understanding about human motor functions at various life stages. These results and technologies are promising to be translated to patient studies in the future.