Neuronal Network Oscillations in the Control of Human Movement

The overarching aim of this thesis was to use neuroimaging and neuromodulation techniques to further understand the relationship between cortical oscillatory activity and the control of human movement. Modulations in motor cortical beta and alpha activity have been consistently implicated in the preparation, execution, and termination of movement. Here, I describe the outcome of four studies designed to further elucidate these motor-related changes in oscillatory activity. In Chapter 3, I report the findings of a study that used an established behavioural paradigm to vary the degree of uncertainty during the preparation of movement. I demonstrate that preparatory alpha and beta desynchronisation reflect a process of disengagement from the existing network to enable the creation of functional assemblies required for movement. Importantly, I also demonstrate a novel neural signature of transient alpha synchrony, that occurs after preparatory desynchronisation, that underlies the recruitment of functional assemblies required for directional control. The study described in Chapter 4 was designed to further investigate the functional role of preparatory alpha and beta desynchronisation by entraining oscillatory activity in the primary motor cortex (M1) using frequency-specific transcranial alternating current stimulation. No significant effects of stimulation were found on participant response times. However, no clear conclusion could be drawn due to limitations of the stimulation parameters that were used. In Chapter 5, I explored the inverse relationship between M1 beta power and cortical excitability using single-pulse transcranial magnetic stimulation to elicit motor-evoked potentials (MEPs). The amplitude of MEPs collected during a period of beta desynchronisation was significantly greater than during a resting baseline. Conversely, the amplitude of MEPs collected during the post-movement beta rebound that follows the termination of a movement was significantly reduced compared to baseline. This finding confirms the inverse relationship between M1 beta power and cortical excitability. The study in Chapter 6 explored the effect of experimental context on M1 beta power. When the participant was cued to expect an upcoming motor task, resting beta power was significantly increased, then when the likelihood of an upcoming motor requirement decreased, there was a significant concurrent decrease in resting beta power. This reflects increased coherence and functional connectivity within M1 and other motor areas, to ‘recalibrate’ the motor system in preparation for a synchronous input signal to more readily recruit the required functional assembly

Heterogeneous recognition of bioacoustic signals for human-machine interfaces

Human-machine interfaces (HMI) provide a communication pathway between man and machine. Not only do they augment existing pathways, they can substitute or even bypass these pathways where functional motor loss prevents the use of standard interfaces. This is especially important for individuals who rely on assistive technology in their everyday life. By utilising bioacoustic activity, it can lead to an assistive HMI concept which is unobtrusive, minimally disruptive and cosmetically appealing to the user. However, due to the complexity of the signals it remains relatively underexplored in the HMI field. This thesis investigates extracting and decoding volition from bioacoustic activity with the aim of generating real-time commands. The developed framework is a systemisation of various processing blocks enabling the mapping of continuous signals into M discrete classes. Class independent extraction efficiently detects and segments the continuous signals while class-specific extraction exemplifies each pattern set using a novel template creation process stable to permutations of the data set. These templates are utilised by a generalised single channel discrimination model, whereby each signal is template aligned prior to classification. The real-time decoding subsystem uses a multichannel heterogeneous ensemble architecture which fuses the output from a diverse set of these individual discrimination models. This enhances the classification performance by elevating both the sensitivity and specificity, with the increased specificity due to a natural rejection capacity based on a non-parametric majority vote. Such a strategy is useful when analysing signals which have diverse characteristics, false positives are prevalent and have strong consequences, and when there is limited training data available. The framework has been developed with generality in mind with wide applicability to a broad spectrum of biosignals. The processing system has been demonstrated on real-time decoding of tongue-movement ear pressure signals using both single and dual channel setups. This has included in-depth evaluation of these methods in both offline and online scenarios. During online evaluation, a stimulus based test methodology was devised, while representative interference was used to contaminate the decoding process in a relevant and real fashion. The results of this research provide a strong case for the utility of such techniques in real world applications of human-machine communication using impulsive bioacoustic signals and biosignals in general

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems