Low Latency Estimation of Motor Intentions to Assist Reaching Movements along Multiple Sessions in Chronic Stroke Patients: A Feasibility Study

Abstract

Background: The association between motor-related cortical activity and peripheral stimulation with temporal precision has been proposed as a possible intervention to facilitate cortico-muscular pathways and thereby improve motor rehabilitation after stroke. Previous studies with patients have provided evidence of the possibility to implement brain-machine interface platforms able to decode motor intentions and use this information to trigger afferent stimulation and movement assistance. This study tests the use a low-latency movement intention detector to drive functional electrical stimulation assisting upper-limb reaching movements of patients with stroke.Methods: An eight-sessions intervention on the paretic arm was tested on four chronic stroke patients along 1 month. Patients' intentions to initiate reaching movements were decoded from electroencephalographic signals and used to trigger functional electrical stimulation that in turn assisted patients to do the task. The analysis of the patients' ability to interact with the intervention platform, the assessment of changes in patients' clinical scales and of the system usability and the kinematic analysis of the reaching movements before and after the intervention period were carried to study the potential impact of the intervention.Results: On average 66.3 ± 15.7% of trials (resting intervals followed by self-initiated movements) were correctly classified with the decoder of motor intentions. The average detection latency (with respect to the movement onsets estimated with gyroscopes) was 112 ± 278 ms. The Fügl-Meyer index upper extremity increased 11.5 ± 5.5 points with the intervention. The stroke impact scale also increased. In line with changes in clinical scales, kinematics of reaching movements showed a trend toward lower compensatory mechanisms. Patients' assessment of the therapy reflected their acceptance of the proposed intervention protocol.Conclusions: According to results obtained here with a small sample of patients, Brain-Machine Interfaces providing low-latency support to upper-limb reaching movements in patients with stroke are a reliable and usable solution for motor rehabilitation interventions with potential functional benefits.

BCI training effects on chronic stroke correlate with functional reorganization in motor-related regions: A concurrent EEG and fMRI study

“…In the end, we explored the relationship between hemodynamic changes 14 revealed by fMRI data and electrophysical changes revealed by EEG data. These 15 findings may provide us with a better understanding of neural mechanisms during 16 stroke recovery from BCI training and with guidance to future experimental design 17 for upper limb. To our best knowledge, this is the first study to utilize concurrent 18 EEG and fMRI to investigate the BCI training effect.…”

“…Studies imply that different 13 behavioral impairments following stroke are related to the disruptions of commu- 14 nication in distributed brain networks with corresponding particular behavioral 15 domains [27]. Furthermore, there is a growing awareness that disrupted functional 16 interactions, especially inter-hemispheric interactions, are highly correlated with 17 motor behavioral deficits and post-stroke recovery [28][29][30][31]. 18 Although correlation-based connectivity using fMRI can investigate the relation- 19 ship between distant brain regions, this kind of connectivity cannot provide any 20 precise information related to the directionality of the information flow due to its 21 poor time resolution.…”

“…To compute α suppression, C3 or C4 channel was chosen according to 16 the subject's lesion side. The EEG data were transferred to frequency domain by a 17 fast Fourier transform with a Hanning window, covering the EEG data during the 18 motor imagery period (6s) in paradigm. The mean power in the α band (8-13 Hz) of 19 the selected electrode was calculated.…”

Investigating The Detection of Intention Signal During Different Exercise Protocols in Robot-Assisted Hand Movement of Stroke Patients and Healthy Subjects Using EEG-BCI System

“…Many studies demonstrate applications of EEG-BCI systems to detect movement intention for various upper-limbs, such as movements of the arm [11][12][13][14][15], elbow [16], [17] and wrist [18] and hand [19][20][21][22][23] for post-stroke rehabilitation.…”

“…The results showed that the True Positive Rate (TPR) of the classifier for healthy participants was 74.5±13.8% while for stroke patients, TPR was 82.2±10.4%. In another study, four chronic post-stroke patients participated in eight training sessions based on arm reaching actions [14]. The intention for the movement was decoded using an EEG-BCI system which then triggered Functional Electrical Stimulation (FES) for further assistance to perform the task.…”