Brain Computer Interface based robotic rehabilitation with online modification of task speed

Abstract

We present a systematic approach that enables online modification/adaptation of robot assisted rehabilitation exercises by continuously monitoring intention levels of patients utilizing an electroencephalogram (EEG) based Brain-Computer Interface (BCI). In particular, we use Linear Discriminant Analysis (LDA) to classify event-related synchronization (ERS) and desynchronization (ERD) patterns associated with motor imagery; however, instead of providing a binary classification output, we utilize posterior probabilities extracted from LDA classifier as the continuous-valued outputs to control a rehabilitation robot. Passive velocity field control (PVFC) is used as the underlying robot controller to map instantaneous levels of motor imagery during the movement to the speed of contour following tasks. In other words, PVFC changes the speed of contour following tasks with respect to intention levels of motor imagery. PVFC also allows decoupling of the task and the speed of the task from each other, and ensures coupled stability of the overall robot patient system. The proposed framework is implemented on AssistOn-Mobile--a series elastic actuator based on a holonomic mobile platform, and feasibility studies with healthy volunteers have been conducted test effectiveness of the proposed approach. Giving patients online control over the speed of the task, the proposed approach ensures active involvement of patients throughout exercise routines and has the potential to increase the efficacy of robot assisted therapies.

A Brain-Machine Interface Based on ERD/ERS for an Upper-Limb Exoskeleton Control

“…Ang et al [25] classified the ERD/ERS patterns as “go” and “rest” using the Common Spatial Pattern algorithm to trigger a 2 degree-of-freedom MIT-Manus robot developed by an MIT research group for reaching tasks. Sarac et al [26] presented a systematic approach that enables online modification/adaptation of robot assisted rehabilitation exercises by continuously monitoring intention levels of patients utilizing an EEG-based BCI. Linear Discriminant Analysis (LDA) was used to classify ERS/ERD patterns associated with MI.…”

Bypassing the Natural Visual-Motor Pathway to Execute Complex Movement Related Tasks Using Interval Type-2 Fuzzy Sets

“…Since the visual-motor pathways are affected for these types of patients, the proper signals cannot be acquired directly from the motor cortex. Thus conventional BCI systems [2], [4], [5], [80], [82], [83], aiming at generation of control commands for rehabilitative aids (such as brain-commanded artificial limbs [Fill me: Saugat Journal paper-P300 based limb]) from the acquired EEG signals captured directly from the motor cortex are unsuitable for the above types of patients. Thus, focusing on the patients with impaired visual-motor coordination due to damaged prefrontal, parietal and/or motor cortex, this paper attempts to derive the mapping of occipital to parietal and prefrontal lobe to motor cortical EEG features from successful instances of visual-motor coordination task and use this mapping in future to offer rehabilitative aids to these patients.…”

“…There exists a lot many works on EEG driven motor planning/control [2], [4], [5], [80], [82], [83]. A few works that require special mention in this regard include EEG driven mind controlled wheelchair [1], [84], [85], brain-actuated asynchronous control of humanoid robots [86], BCI based unmanned car control [88], virtual gaming [89] and other applications [82].…”

Pre-movement contralateral EEG low beta power is modulated with motor adaptation learning

“…We argue that these findings can be used as a biomarker for current BCI-assisted stroke rehabilitation approaches. In such protocols, BCIs are often used to decode movement intent from EEG data that is synchronized to a rehabilitation robot with haptic feedback to provide movement support during rehabilitation exercises [23][24][25]. Similarly during rehabilitation exercises a BCI can monitor the EEG of the patient, and provide movement support whenever an individually spatially and spectrally characterized increase of pre-movement EEG activity is detected, with the goal of supporting motor learning.…”