Corticomuscular Co-Activation Based Hybrid Brain-Computer Interface for Motor Recovery Monitoring

Abstract

The effect of corticomuscular coactivation based hybrid brain-computer interface (h-BCI) on post-stroke neurorehabilitation has not been explored yet. A major challenge in this area is to find an appropriate corticomuscular feature which can not only drive an h-BCI but also serve as a biomarker for motor recovery monitoring. Our previous study established the feasibility of a new method of measuring corticomuscular co-activation called correlation of band-limited power time-courses (CBPT) of EEG and EMG signals, outperforming the traditional EEG-EMG coherence in terms of accurately controlling a robotic hand exoskeleton device by the stroke patients. In this paper, we have evaluated the neurophysiological significance of CBPT for motor recovery monitoring by conducting a 5-week long longitudinal pilot trial on 4 chronic hemiparetic stroke patients. Results show that the CBPT variations correlated significantly (p-value< 0.05) with the dynamic changes in motor outcome measures during the therapy for all the patients. As the bandpower based biomarkers are popular in literature, a comparison with such biomarkers has also been made to cross-verify whether the changes in CBPT are indeed neurophysiological. Thus the study concludes that CBPT can serve as a biomarker for motor recovery monitoring while serving as a corticomuscular co-activation feature for h-BCI based neurorehabilitation. Despite an observed significant positive change between pre-and post-intervention motor outcomes, the question of the clinical effectiveness of CBPT is subject to further controlled trial on a larger cohort.

Determining optimal mobile neurofeedback methods for motor neurorehabilitation in children and adults with non-progressive neurological disorders: a scoping review

“…Only four studies targeted the lower limbs [ 26 , 61 , 70 , 77 ], all focused on ankle dorsiflexion. The upper limb motor tasks included shoulder ab/adduction (n = 1) [ 63 ], reaching (n = 5) [ 59 , 60 , 65 , 68 , 74 ], grasping (n = 5) [ 4 , 41 , 64 , 66 , 67 ], reaching and grasping (n = 2) [ 71 , 73 ], hand and wrist extension (n = 2) [ 22 , 23 ], and finger extension (n = 4) [ 69 , 72 , 75 , 76 ].…”

“…The number of training sessions varied from 1 to 80 sessions, with six studies below 10 sessions [ 60 , 61 , 68 , 71 , 72 , 77 ], 11 between 10 to 15 sessions [ 22 , 23 , 26 , 41 , 59 , 65 – 67 , 69 , 70 , 75 ], and six with more than 15 sessions [ 4 , 63 , 64 , 73 , 74 , 76 ]. It is noteworthy that Takahashi et al [ 77 ] and Mrachacz-Kersting et al [ 61 ] demonstrated improvements after only one session of BCI-NFT.…”

“…In a unique approach, Remsik et al electrically stimulated the tongue along with visual and FES feedback to potentially enhance sensory input to cortical regions [ 41 ]. Robotic devices were used to provide proprioceptive feedback as well as motor assistance in seven studies, combined with visual feedback (n = 4) [ 59 , 60 , 66 , 67 ] or alone in three studies [ 4 , 23 , 65 ]. Visual feedback was used alone in two studies [ 69 , 71 ].…”

A Sliding Window Common Spatial Pattern for Enhancing Motor Imagery Classification in EEG-BCI

“…BCI is also being used for motor recovery in patients who have suffered from a stroke or other injury which often leaves them with the inability to move. This is done by measuring changes in neuronal activity over the motor cortex during a MI task whilst providing them with a reward in the form of a virtual avatar performing the same movement and/or with a robotic device [4], [5], [6], [7].…”

Clinical Brain–Computer Interface Challenge 2020 (CBCIC at WCCI2020): Overview, Methods and Results

“…The BCI Competition IV was especially focused on the oscillatory brain activity associated with different motor tasks such as MI and motor execution (ME) [4]. This is because a major application area of BCI technology is in rehabilitating the motor impairments of neurologically disabled people by promoting neuroplasticity [6], [7]. There were 5 different datasets in this competition across three different brain modalities such as EEG, MEG, and ECoG.…”