The Analysis of Surface EMG Signals with the Wavelet-Based Correlation Dimension Method

Many attempts have been made to effectively improve a prosthetic system controlled by the classification of surface electromyographic (SEMG) signals. Recently, the development of methodologies to extract the effective features still remains a primary challenge. Previous studies have demonstrated that the SEMG signals have nonlinear characteristics. In this study, by combining the nonlinear time series analysis and the time-frequency domain methods, we proposed the wavelet-based correlation dimension method to extract the effective features of SEMG signals. The SEMG signals were firstly analyzed by the wavelet transform and the correlation dimension was calculated to obtain the features of the SEMG signals. Then, these features were used as the input vectors of a Gustafson-Kessel clustering classifier to discriminate four types of forearm movements. Our results showed that there are four separate clusters corresponding to different forearm movements at the third resolution level and the resulting classification accuracy was 100%, when two channels of SEMG signals were used. This indicates that the proposed approach can provide important insight into the nonlinear characteristics and the time-frequency domain features of SEMG signals and is suitable for classifying different types of forearm movements. By comparing with other existing methods, the proposed method exhibited more robustness and higher classification accuracy

Cortical motor network modulation: Common mechanisms parallel efficient motor integration in implicit motor learning in healthy subjects and subthalamic neurostimulation in Parkinson’s disease

On the one hand, the neuronal circuitry and connectivity of the large-scale motor network play an important role in many human cognitive functions, i. e. in implicit motor learning. On the other hand, alterations in connectivity of the motor network are also a hallmark in the pathophysiology of a variety of psychological and neurological diseases, such as Parkinson’s disease. Here, we set out to study the motor network activity (more exactly the cortical and spinal aspects of it) under two different aspects: in healthy controls during implicit motor learning and in Parkinson’s disease patients in the conditions ‘stimulation off’ and ‘stimulation on’. To this end, 12 healthy controls and 20 Parkinson’s disease patients performed externally paced right finger movements with simultaneous recordings of a 64-channel EEG and EMG of the forearm muscles. The healthy controls performed the serial reaction time task. Parkinson’s disease patients conducted the baseline of this task with only random trials in the two conditions ‘stimulation off’ and ‘stimulation on ‘. Cortical and muscular activity was analyzed by time-frequency movement-related spectral perturbations and by power spectral density and corticospinal synchronization was assessed by time-frequency cross-spectra coherence. Clinically, Parkinson’s disease patients improved significantly with deep brain stimulation, assessed by the Unified Parkinson’s Disease Rating Scale III score, the reaction time and the error ratio. Deep brain stimulation lead to an increased cortical beta-band movement-related desynchronization, which was topographically spread over a wider cortical area. Besides, in ‘stimulation off’ after finger tap we found a premature beta-band rebound of the corticomuscular coherence to the extensor digitorum over the primary sensorimotor cortex, which was suppressed with stimulation on. The healthy controls presented with significantly reduced reaction times in the ‘sequence blocks’ compared to ‘random blocks’. In ‘sequence blocks’, power spectral density increased mainly over the right posterior parietal cortex but also over a larger left-hemispheric cortical area in alpha and low beta band. Alpha and beta band movement-related desynchronization presented most pronounced over the bilateral prefrontal, fronto-central and central channels. The movement-related desynchronization was significantly modulated over the course of implicit motor learning. The present findings reveal the impressive modulation of the motor network activity including cortical activations and corticospinal synchronizations introduced by deep brain stimulation therapy of the subthalamic nucleus in Parkinson’s disease

EEG Coherence and Executive Function in Mild Cognitive Impairment and Alzheimer’s Disease: An Examination of Resting Coherence and Coherence During Executive Functioning Tasks

Deficits in executive functioning have been reported in the early stages of Alzheimer’s disease (AD) and in mild cognitive impairment (MCI); however, the neural underpinnings of these deficits remain unclear. It has been proposed that AD can be characterized as a disconnection syndrome, where functional connectivity between brain regions is compromised. Therefore, it may be hypothesized that altered functional connectivity may be related to executive functioning in MCI and AD. The research presented in this dissertation examined group differences for MCI and AD patients relative to controls for EEG coherence within a fronto-parietal network measured at rest (Study 1), during a Go/No-go inhibitory control task (Study 2), and during an N-back working memory task (Study 3). The relationships between coherence and measures of cognition and brain integrity (cortical thickness and PiB retention) were also explored. Results indicated that AD patients, but not MCI patients, had reduced resting coherence between cross-hemisphere parietal regions versus normal controls, and that MCI patients who later converted to dementia had higher resting fronto-parietal coherence versus MCI patients who remained stable. Furthermore, both AD and MCI patients showed altered coherence during task performance. During both tasks, AD patients showed reduced coherence and less of a task-related increase in coherence versus controls (for cross-hemisphere electrode pairs during the Go/No-go task and for cross-hemisphere and fronto-parietal pairs during the N-back task). In contrast, in comparison to controls, MCI patients had higher fronto-parietal coherence during the Go/No-go task and a larger task-related increase in fronto-parietal coherence for both tasks, but less of a task-related increase in cross-hemisphere frontal coherence for both tasks. Correlational analyses showed different relationships between EEG coherence and cognition and brain integrity across groups, with some evidence of a potential compensatory mechanism for higher coherence in controls and MCI patients in some conditions. These results demonstrate that functional connectivity within a fronto-parietal network is altered in AD patients and MCI patients during the performance of executive tasks. In AD patients, coherence is decreased, whereas MCI patients show a potential compensatory increase in fronto-parietal coherence. The implications of these findings and directions for future research are discussed