{"id":{"repo_id":"toronto-retro","oai_identifier":"oai:utoronto.scholaris.ca:1807/72375"},"canonical_url":"https://search.dev.ndltd.org/etd/toronto-retro/oai:utoronto.scholaris.ca:1807/72375","repository":{"repo_id":"toronto-retro","name":"University of Toronto","base_url":"https://utoronto.scholaris.ca/server/oai/request"},"display":{"title":"Electrophysiological Recording of the Brain, Visualization, Prediction, and Interconnectivity","abstract":"The human brain is a complex network of interconnected neurons. The aim of neuroscience and neuroengineering is to decode the neural activity, visualize it and try to better understand how neurons communicate with each other. This dissertation comprises four contributions to the area. These four topics are discussing how functional relationship between brain activity and movement can be found and whether common features found in different regions are correlated (phase-locked). First, the method of chirplet decomposition offers a new way to visualize the time-frequency content of non-stationary signals with higher resolution than previously possible. The use of Wigner-Ville distribution together with chirplet decomposition allows a clearer visualization in terms of both the temporal and frequency details with detail higher than previously achieved using other methods including Choi-Williams and spectrogram. Second, an improved method of averaging of neural signals over repeated trials is introduced whereby slight variations in the alignment of the neural signal over time is corrected through the use of nonlinear shifts. In earlier studies, time alignment has been performed using linear shift (e.g. alignment with movement onset), but this process alone is not sufficient when the signal timing changes differently over time. To overcome this issue, nonlinear transformations were found to remove any temporal variabilities in the way the task was performed. Third, a multilinear model is demonstrated showing how limb velocity in a reach task can be predicted from neuroelectrical activity. The model, after fitting, suggested that high frequency oscillations have sufficient information for both detection of movement onset and reconstruction of its movement. The use of a linear model reduces the overall computational requirements and simplifies the reconstruction of movement kinematics. Finally, a fourth method involving the measurement of coherence over time reveals how circuits in the basal ganglia communicate with the cortical layers during voluntary movements. This allows investigating the inter-coupling between the sensorimotor cortex and basal ganglia and how cortico-basal ganglia coupling changes with respect to the movement. The association of coherence with power suggests that a coupling in neural activity between the basal ganglia and the cortical region of the brain is required for the execution of voluntary movements. The coherent activity suggests that similar information can be found in two brain regions.","abstract_html":"The human brain is a complex network of interconnected neurons. The aim of neuroscience and neuroengineering is to decode the neural activity, visualize it and try to better understand how neurons communicate with each other. This dissertation comprises four contributions to the area. These four topics are discussing how functional relationship between brain activity and movement can be found and whether common features found in different regions are correlated (phase-locked). First, the method of chirplet decomposition offers a new way to visualize the time-frequency content of non-stationary signals with higher resolution than previously possible. The use of Wigner-Ville distribution together with chirplet decomposition allows a clearer visualization in terms of both the temporal and frequency details with detail higher than previously achieved using other methods including Choi-Williams and spectrogram. Second, an improved method of averaging of neural signals over repeated trials is introduced whereby slight variations in the alignment of the neural signal over time is corrected through the use of nonlinear shifts. In earlier studies, time alignment has been performed using linear shift (e.g. alignment with movement onset), but this process alone is not sufficient when the signal timing changes differently over time. To overcome this issue, nonlinear transformations were found to remove any temporal variabilities in the way the task was performed. Third, a multilinear model is demonstrated showing how limb velocity in a reach task can be predicted from neuroelectrical activity. The model, after fitting, suggested that high frequency oscillations have sufficient information for both detection of movement onset and reconstruction of its movement. The use of a linear model reduces the overall computational requirements and simplifies the reconstruction of movement kinematics. Finally, a fourth method involving the measurement of coherence over time reveals how circuits in the basal ganglia communicate with the cortical layers during voluntary movements. This allows investigating the inter-coupling between the sensorimotor cortex and basal ganglia and how cortico-basal ganglia coupling changes with respect to the movement. The association of coherence with power suggests that a coupling in neural activity between the basal ganglia and the cortical region of the brain is required for the execution of voluntary movements. 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The aim of neuroscience and neuroengineering is to decode the neural activity, visualize it and try to better understand how neurons communicate with each other. This dissertation comprises four contributions to the area. These four topics are discussing how functional relationship between brain activity and movement can be found and whether common features found in different regions are correlated (phase-locked). First, the method of chirplet decomposition offers a new way to visualize the time-frequency content of non-stationary signals with higher resolution than previously possible. The use of Wigner-Ville distribution together with chirplet decomposition allows a clearer visualization in terms of both the temporal and frequency details with detail higher than previously achieved using other methods including Choi-Williams and spectrogram. Second, an improved method of averaging of neural signals over repeated trials is introduced whereby slight variations in the alignment of the neural signal over time is corrected through the use of nonlinear shifts. In earlier studies, time alignment has been performed using linear shift (e.g. alignment with movement onset), but this process alone is not sufficient when the signal timing changes differently over time. To overcome this issue, nonlinear transformations were found to remove any temporal variabilities in the way the task was performed. Third, a multilinear model is demonstrated showing how limb velocity in a reach task can be predicted from neuroelectrical activity. The model, after fitting, suggested that high frequency oscillations have sufficient information for both detection of movement onset and reconstruction of its movement. The use of a linear model reduces the overall computational requirements and simplifies the reconstruction of movement kinematics. Finally, a fourth method involving the measurement of coherence over time reveals how circuits in the basal ganglia communicate with the cortical layers during voluntary movements. This allows investigating the inter-coupling between the sensorimotor cortex and basal ganglia and how cortico-basal ganglia coupling changes with respect to the movement. The association of coherence with power suggests that a coupling in neural activity between the basal ganglia and the cortical region of the brain is required for the execution of voluntary movements. The coherent activity suggests that similar information can be found in two brain regions."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Electrophysiological Recording of the Brain, Visualization, Prediction, and Interconnectivity"]}]}],"canonical_facts":{"dc:contributor.advisor":["Wong, Willy"],"dc:contributor.department":["Electrical and Computer Engineering"],"dc:creator":["Talakoub, Omid"],"dc:date":["2014-11"],"dc:date.accessioned":["2016-05-19T21:03:05Z"],"dc:date.available":["2016-05-19T21:03:05Z"],"dc:date.issued":["2014-11"],"dc:description.abstract":["The human brain is a complex network of interconnected neurons. The aim of neuroscience and neuroengineering is to decode the neural activity, visualize it and try to better understand how neurons communicate with each other. This dissertation comprises four contributions to the area. 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This allows investigating the inter-coupling between the sensorimotor cortex and basal ganglia and how cortico-basal ganglia coupling changes with respect to the movement. The association of coherence with power suggests that a coupling in neural activity between the basal ganglia and the cortical region of the brain is required for the execution of voluntary movements. The coherent activity suggests that similar information can be found in two brain regions."],"dc:description.degree":["Ph.D."],"dc:identifier.uri":["http://hdl.handle.net/1807/72375"],"dc:title":["Electrophysiological Recording of the Brain, Visualization, Prediction, and Interconnectivity"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T21:28:05Z"}