Browsing by Author "John, Lester R"

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    Open Access
    Investigation of the interaction between corticomuscular coherence, motor precision and perceived difficulty in wrist flexion and extension
    (2013) Divekar, Nikhil Vishwas; John, Lester R
    Recently, behavioural (motor precision) differences were reported between isometric wrist flexion and extension. Neurophysiological as well as clinical differences have also been reported between these antagonistic movements. Corticomuscular coherence (CMC), i.e. the frequency specific temporal coupling between the electroencephalogram (EEG) and electromyogram (EMG) recorded during isometric force production, reflects the functional connectivity between cortex and muscle. A single muscle (flexor digitorum superficialis) study suggests a positive correlation between 15-35 Hz (beta) CMC and motor precision of the muscle. Yet, no study has simultaneously compared CMC and motor precision between wrist flexion and extension. Task perceived difficulty, which is a perceptual variable, may influence both motor precision and CMC, but has not been studied yet. The main aim of the present study was to investigate the interaction between CMC, motor precision and perceived difficulty in isometric wrist flexion and extension tasks.
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    On the relationship between corticomuscular (EEG-EMG) phase coupling and muscular fatigue
    (2015) Joseph, Jeff Varkey Joshy; John, Lester R; Albertus-Kajee, Yumna
    Contradictory results have been shown in studies measuring the effect of muscle fatigue on the level of synchrony between the oscillatory, cortical and muscular electrical activities (also known as corticomuscular coupling). In every study, the standard method (coherence) used to measure the level of synchrony takes into account both the amplitude and phase of the two signals. However, the use of the phase lock value (PLV) has been over looked as a method for determining the level of synchrony. While the PLV is modulated purely by the phase between the two signals, it is unaffected by any amplitude variation. This study aims to determine whether amplitude variations in electroencephalography (EEG) and electromyography (EMG) could have caused the contradictory results when comparing pre-,during and post-fatigue measures of corticomuscular coupling, which consequently affected the conclusions drawn regarding the monitoring of fatigue by the central nervous system. A determination will be made regarding the contradictions by directly comparing the two methods (coherence and PLV) on the same dataset of simultaneously measured EEG and EMG signals throughout an isometric pre-, during and post-fatigue task.
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    A supervised machine-learning method for detecting steady-state visually evoked potentials for use in brain computer interfaces: A comparative assessment
    (2017) Duggan, Kieran Eamon; Meintjes, Ernesta M; De Jager, Kylie; John, Lester R
    It is hypothesised that supervised machine learning on the estimated parameters output by a model for visually evoked potentials (VEPs), created by Kremlácek et al. (2002), could be used to classify steady-state visually evoked potentials (SSVEP) by frequency of stimulation. Classification of SSVEPs by stimulus frequency has application in SSVEP-based brain computer interfaces (BCI), where users are presented with flashing stimuli and user intent is decoded by identifying which stimulus the subject is attending to. We investigate the ability of the model of VEPs to fit the initial portions of SSVEPs, which are not yet in a steady state and contain characteristic features of VEPs superimposed with those of a steady state response. In this process the estimated parameters, as a function of the model for a given SSVEP response, were found. These estimated parameters were used to train several support vector machines (SVM) to classify the SSVEPs. Three initialisation conditions for the model are examined for their contribution to the goodness of fit and the subsequent classification accuracy, of the SVMs. It was found that the model was able to fit SSVEPs with a normalised root mean square error (NRMSE) of 27%, this performance did not match the expected NRMSE values of 13% reported by Kremlácek et al. (2002) for fits on VEPs. The fit data was assessed by the machine learning scheme and generated parameters which were classifiable by SVM above a random chance of 14% (Reang 9% to 28%). It was also shown that the selection of initial parameters had no distinct effect on the classification accuracy. Traditional classification approaches using spectral techniques such as Power Spectral Density Analysis (PSDA) and canonical correlation analysis (CCA) require a window period of data above 1 s to perform accurately enough for use in BCIs. The larger the window period of SSVEP data used the more the Information transfer rate (ITR) decreases. Undertaking a successful classification on only the initial 250 ms portions of SSVEP data would lead to an improved ITR and a BCI which is faster to use. Classification of each method was assessed at three SSVEP window periods (0.25, 0.5 and 1 s). Comparison of the three methods revealed that, on a whole CCA outperformed both the PSDA and SVM methods. While PSDA performance was in-line with that of the SVM method. All methods performed poorly at the window period of 0.25 s with an average accuracy converging on random chance - 14%. At the window period of 0.5 s the CCA only marginally outperformed the SVM method and at a time of 1 s the CCA method significantly (p<0.05) outperformed the SVM method. While the SVMs tended to improve with window period the results were not generally significant. It was found that certain SVMs (Representing a unique combination of subject, initial conditions and window period) achieved an accuracy as high as 30%. For a few instances the accuracy was comparable to the CCA method with a significance of 5%. While we were unable to predict which SVM would perform well for a given subject, it was demonstrated that with further refinement this novel method may produce results similar to or better than that of CCA.
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    Towards the development of a dynamic device for the evaluation of hypertonia of the upper extremity
    (2014) Proxenos, Matthew; John, Lester R
    Traditional evaluation techniques for spastic hypertonia, such as the Modified Ashworth Scale (MAS), are prone to subjectivity and have been shown to have poor inter- and intra-rater reliability. Automated objective electromechanical devices for upper-limb evaluation do exist, such as the commercially available NeuroFlexor device. These assess combined wrist and finger flexor tone by monitoring wrist joint torque during passive wrist extension. Wrist flexor tone evaluations made by manipulation of the wrist joint alone, however, could be affected by possible hypertonia of the finger flexors due to the moment arm that these muscles‟ tendons have at the wrist joint. As such, robotic wrist flexor evaluation devices that measure only the wrist joint torque cannot distinguish between wrist and finger flexor hypertonia. A robotic device measuring involuntary resistance at the wrist and finger joints separately during wrist manipulation can be used to provide wrist flexor tone assessments that compensate for the influence of hypertonia of the finger flexor muscles, and therefore provide more accurate tone assessments of the wrist flexor muscles. To design, construct and evaluate a patient-safe device for the independent measurement of wrist and finger joint torque during wrist extension, and to use the device to accurately evaluate wrist flexor tone, in isolation from possible effects of finger flexor tone. Evaluations were made using the device in a clinical setting with volunteers (n=6) with varying levels of hypertonia in the hands and wrists. Volunteers’ wrist flexor tone was also assessed by three clinicians using the MAS score.