Detecting movement intent from scalp EEG in a novel upper limb robotic rehabilitation system for stroke

Abstract

Stroke can be a source of significant upper extremity dysfunction and affect the quality of life (QoL) in survivors. In this context, novel rehabilitation approaches employing robotic rehabilitation devices combined with brain-machine interfaces can greatly help in expediting functional recovery in these individuals by actively engaging the user during therapy. However, optimal training conditions and parameters for these novel therapeutic systems are still unknown. Here, we present preliminary findings demonstrating successful movement intent detection from scalp electroencephalography (EEG) during robotic rehabilitation using the MAHI Exo-II in an individual with hemiparesis following stroke. These findings have strong clinical implications for the development of closed-loop brain-machine interfaces to robotic rehabilitation systems.

Figures

Fig. 1

User with left-sided hemiparesis fitted with MAHI Exo-II and the EEG-EMG sensors; inset shows the GUI which feed-backs current exoskeleton position to the user.

Fig. 2

Sequence of each trial. Each trial starts when the subject enters the center position and a fixation cross is displayed for 4 to 6s. Two targets (Up & Down) appear on the screen, at which time the subject selects a target, prepares and later executes the movement. Unknown to the subject, target selection and preparation times of less than 2s result in an aborted trial, which can be restarted by re-entering the center position. In a successful trial, the subject performs elbow flexion/extension to hit the target, following which the robot automatically returns the subject’s hand to the center.

Fig. 3

Top row shows segment of median EMG envelope during Up/Down movements for the stroke participant (S1) and one healthy participant (H3). Bottom four rows show baseline corrected, grand average MRCP during four training modes with the MAHI Exo-II for six relevant frontal and central channels. Additionally, (t = 0s) corresponds to the movement onset time when the joint velocity threshold was exceeded. Dashed vertical lines indicate ‘Go’ and ‘No-Go’ windows optimally selected for each subject.

Fig. 4

Boxplots showing median classification accuracies across 4 training modes for all participants (interquartile and full ranges shown along with outliers as ‘+’). BD: Backdrive; P: Passive; T: Triggered; O: Observation.