Martin Wieser

Temporal and spatial patterns of cortical activation during assisted lower limb movement

Human gait is a complex process in the central nervous system that results from the integrity of various mechanisms, including different cortical and subcortical structures. In the present study, we investigated cortical activity during lower limb movement using EEG. Assisted by a dynamic tilt table, all subjects performed standardized stepping movements in an upright position. Source localization of the movement-related potential in relation to spontaneous EEG showed activity in brain regions classically associated with human gait such as the primary motor cortex, the premotor cortex, the supplementary motor cortex, the cingulate cortex, the primary somatosensory cortex and the somatosensory association cortex. Further, we observed a task-related power decrease in the alpha and beta frequency band at electrodes overlying the leg motor area. A temporal activation and deactivation of the involved brain regions as well as the chronological sequence of the movement-related potential could be mapped to specific phases of the gait-like leg movement. We showed that most cortical capacity is needed for changing the direction between the flexion and extension phase. An enhanced understanding of the human gait will provide a basis to improve applications in the field of neurorehabilitation and brain–computer interfaces.

Model-based Heart rate prediction during Lokomat walking

We implemented a model for prediction of heart rate during Lokomat walking. Using this model, we can predict potential overstressing of the patient and adapt the physical load accordingly. Current models for treadmill based heart rate control neglect the fact that the interaction torques between Lokomat and human can have a significant effect on heart rate. Tests with five healthy subjects lead to a model of sixth order with walking speed and power expenditure as inputs and heart rate prediction as output. Recordings with five different subjects were used for model validation. Future work includes model identification and predictive heart rate control with spinal cord injured and stroke patients.

Bio-cooperative robotics: Controlling mechanical, physiological and mental patient states

In classical man-machine interfaces, biomechanics of the human form a part of the underlying control loop. However, integrating the human into the loop can be considered not only from a biomechanical view but also with regard to psycho-physiological aspects. Biomechanical integration involves ensuring that the system to be used is ergonomically acceptable and “user-cooperative”. Psycho-physiological integration involves recording and controlling the patients physiological reactions so that the patient receives appropriate stimuli and is challenged in a moderate but engaging way without causing undue stress or harm. In this paper, we present examples of biomechanical and psycho-physiological integration of patients verified with the gait robot Lokomat and the dynamic tilt and stepping device Erigo.

Quantitative description of the state of awareness of patients in vegetative and minimally conscious state

Clinical scales represent the golden standard in characterizing awareness for patients in vegetative or in a minimally conscious state. Clinical scales suffer from problems of sensitivity, specificity, subjectivity, and inter-rater reliability. This leads to a misdiagnosis rate of up to 40% and consequences associated with inappropriate treatment decisions. In this study, objective measures including physiological and neurological signals are used to quantify the patient status. Using linear backward regression analysis, 13 variables (based on frequency analysis of the electrocardiogram, heart rate variability, amplitude and latency of the P300, skin conductance responses, changes in the blood pressure and respiration signal) were found to be sufficient to describe 74.7% of the variability of the scores. In this regression model, the P300, electrocardiogram and the blood pressure signal account for most of the variability. More patient data and additional measures will enable refinement of the methods. This new objective-measurement based model of the state of awareness will complement the clinical scales in order to increase the quality of diagnosis.

Automation in Rehabilitation: How to Include the Human into the Loop

Within rehabilitation robotic applications, integrating the human into the loop can be considered not only from a biomechanical view but also with regard to psycho-physiological aspects. Biomechanical integration involves ensuring that the system to be used is ergonomically acceptable and “user-cooperative”. Psycho-physiological integration involves recording and controlling the patient’s physiological reactions so that the patient receives appropriate stimuli and is challenged in a moderate but engaging and motivating way without causing undue stress or harm. In this paper, we present examples of biomechanical and psycho-physiological integration of patients verified with the gait robot Lokomat and the dynamic tilt and stepping device Erigo.

Quantification of clinical scores through physiological recordings in low-responsive patients: a feasibility study

Clinical scores represent the gold standard in characterizing the clinical condition of patients in vegetative or minimally conscious state. However, they suffer from problems of sensitivity, specificity, subjectivity and inter-rater reliability.In this feasibility study, objective measures including physiological and neurophysiological signals are used to quantify the clinical state of 13 low-responsive patients. A linear regression method was applied in nine patients to obtain fixed regression coefficients for the description of the clinical state. The statistical model was extended and evaluated with four patients of another hospital. A linear mixed models approach was introduced to handle the challenges of data sets obtained from different locations.Using linear backward regression 12 variables were sufficient to explain 74.4% of the variability in the change of the clinical scores. Variables based on event-related potentials and electrocardiogram account for most of the variability.These preliminary results are promising considering that this is the first attempt to describe the clinical state of low-responsive patients in such a global and quantitative way. This new model could complement the clinical scores based on objective measurements in order to increase diagnostic reliability. Nevertheless, more patients are necessary to prove the conclusions of a statistical model with 12 variables.

Sleep-wake behaviour and the EEG in altered states of consciousness

Abstract presented at the 23rd Australasian Society for Psychophysiology Conference, 20-22 Nov 2013, Wollongong, Australia. Disciplines Medicine and Health Sciences Publication Details Loughran, S., Regel, S., Buetler, L., Wieser, M., Riener, R. & Achermann, P. (2013). Sleep-wake behaviour and the EEG in altered states of consciousness. 23rd Australasian Society for Psychophysiology Conference (p. 43). Wollongong, Australia: Australian Society for Psychophysiology Inc. Authors Sarah P. Loughran, Sabine Regel, Lilith Buetler, Martin Wieser, Robert Riener, and Peter Achermann This conference paper is available at Research Online: http://ro.uow.edu.au/ihmri/431