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|Title:||New environments for neurophysiological investigations.|
|Authors:||Sehmi, Arvindra Singh.|
|Presented at:||University of Leicester|
|Abstract:||The main topics of research are in the sub-areas of neurophysiology that are concerned with measurement of the electrical activity arising from contracting muscle (EMG) and from the surface of the scalp (EEG). Investigations are restricted to the surface-recorded interference pattern EMG, and to the EEG waveform recorded in response to sensory stimulation, known as the evoked potential (EP). The EMG and EP are representative of two important classes of signal commonly encountered in engineering, namely random noise-like and deterministic non-stationary. The thesis describes work on the development of a variety of new techniques and methods of analysis for application in neurophysiology and electrodiagnosis. A general purpose signal processing computer has been built which incorporates a high level of user-machine ergonomics. Turning Points Spectral estimation of the interference pattern EMG is simulated on this computer to demonstrate its flexibility for constructing analysis and control applications. Some emphasis is placed on methods of improving the quality of acquired EMG data for use in the analysis of the dynamics of the neuromuscular system. In this respect, the author describes the design of a fully controllable muscle loading system which uses dc electromagnetic suspension technology. The above computer can be used to control this muscle load for accurate loading protocols in EMG-Force modelling experiments. Techniques involved in the design and construction of the computer lead to higher-level program and data analysis specifications which employ Artificial Intelligence (AI) computing methods. These AI methods, in conjunction with some of those techniques which were used for EMG analysis, are applied to the investigation of single-trial EPs. A suite of adaptive EP analysis procedures, which include a prototype fuzzy expert system, facilitate the extraction of EP component latency variability estimates, and also provide automatic selective single-trial averaging. The latter selective averaging facility, can be used to enhance underlying activity and to examine the relationships that might exist between different components in the EP.|
|Rights:||Copyright © the author. All rights reserved.|
|Appears in Collections:||Theses, Dept. of Engineering|
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