Deep Learning of Movement Intent and Reaction Time for EEG-informed Adaptation of Rehabilitation Robots
This work addresses the need for objective adaptation in rehabilitation robots for patients with sensorimotor impairments, though it is incremental as it builds on existing EEG and robotic methods.
The paper tackled the problem of using robot-derived movement kinematics for rehabilitation robot adaptation, which can be subjective, by proposing a deep CNN that uses EEG to objectively measure movement intent and reaction time. The CNN achieved average test accuracies of 80.08% for intent classification and 79.82% for reaction time classification on data from 13 subjects.
Mounting evidence suggests that adaptation is a crucial mechanism for rehabilitation robots in promoting motor learning. Yet, it is commonly based on robot-derived movement kinematics, which is a rather subjective measurement of performance, especially in the presence of a sensorimotor impairment. Here, we propose a deep convolutional neural network (CNN) that uses electroencephalography (EEG) as an objective measurement of two kinematics components that are typically used to assess motor learning and thereby adaptation: i) the intent to initiate a goal-directed movement, and ii) the reaction time (RT) of that movement. We evaluated our CNN on data acquired from an in-house experiment where 13 subjects moved a rehabilitation robotic arm in four directions on a plane, in response to visual stimuli. Our CNN achieved average test accuracies of 80.08% and 79.82% in a binary classification of the intent (intent vs. no intent) and RT (slow vs. fast), respectively. Our results demonstrate how individual movement components implicated in distinct types of motor learning can be predicted from synchronized EEG data acquired before the start of the movement. Our approach can, therefore, inform robotic adaptation in real-time and has the potential to further improve one's ability to perform the rehabilitation task.