Classification of motor task execution speed from EEG data

It is believed that the obtention of instantaneous intention level from electroencephalogram (EEG) signals and its use as a control signal may increase the benefits gained from the robotic rehabilitation process of stroke patients. This paper investigates a method for classifying the speed of arm movements from EEG recordings of healthy subjects under the assumption that the intention level of a patient may be reflected in motor task execution velocity. Experimental data were collected from eight (four male, four female) healthy volunteers while they were performing right arm movements at two different speeds. We designed an experiment in which the subjects were asked to carry a glass cup in two different environments: nail or cotton. The task speeds for both environments were decided individually by the volunteers; however the nail environment had a maximum speed limit. Participants were warned by a crashing glass audio stimulus if they exceeded the speed limit of the nail environment. As a result, a simple daily life activity was performed at two different speeds as an experimental task. Based on experimental data from eight healthy subjects, we successfully classified two different speed levels and resting state from event related synchronization (ERS) and event related desynchronization (ERD) patterns of EEG signals by linear discriminant analysis (LDA) classifier. Results reveal that LDA can discriminate different velocity levels when six frequency bands of three EEG recording channels were used as the feature vector

Enhancement of Robot-Assisted Rehabilitation Outcomes of Post-Stroke Patients Using Movement-Related Cortical Potential

Post-stroke rehabilitation is essential for stroke survivors to help them regain independence and to improve their quality of life. Among various rehabilitation strategies, robot-assisted rehabilitation is an efficient method that is utilized more and more in clinical practice for motor recovery of post-stroke patients. However, excessive assistance from robotic devices during rehabilitation sessions can make patients perform motor training passively with minimal outcome. Towards the development of an efficient rehabilitation strategy, it is necessary to ensure the active participation of subjects during training sessions. This thesis uses the Electroencephalography (EEG) signal to extract the Movement-Related Cortical Potential (MRCP) pattern to be used as an indicator of the active engagement of stroke patients during rehabilitation training sessions. The MRCP pattern is also utilized in designing an adaptive rehabilitation training strategy that maximizes patients’ engagement. This project focuses on the hand motor recovery of post-stroke patients using the AMADEO rehabilitation device (Tyromotion GmbH, Austria). AMADEO is specifically developed for patients with fingers and hand motor deficits. The variations in brain activity are analyzed by extracting the MRCP pattern from the acquired EEG data during training sessions. Whereas, physical improvement in hand motor abilities is determined by two methods. One is clinical tests namely Fugl-Meyer Assessment (FMA) and Motor Assessment Scale (MAS) which include FMA-wrist, FMA-hand, MAS-hand movements, and MAS-advanced hand movements’ tests. The other method is the measurement of hand-kinematic parameters using the AMADEO assessment tool which contains hand strength measurements during flexion (force-flexion), and extension (force-extension), and Hand Range of Movement (HROM)