Motor imagery brain–computer interface rehabilitation system enhances upper limb performance and improves brain activity in stroke patients: A clinical study

Abstract

This study compared the efficacy of Motor Imagery brain-computer interface (MI-BCI) combined with physiotherapy and physiotherapy alone in ischemic stroke before and after rehabilitation training. We wanted to explore whether the rehabilitation effect of MI-BCI is affected by the severity of the patient’s condition and whether MI-BCI was effective for all patients. Forty hospitalized patients with ischemic stroke with motor deficits participated in this study. The patients were divided into MI and control groups. Functional assessments were performed before and after rehabilitation training. The Fugl-Meyer Assessment (FMA) was used as the primary outcome measure, and its shoulder and elbow scores and wrist scores served as secondary outcome measures. The motor assessment scale (MAS) was used to assess motor function recovery. We used non-contrast CT (NCCT) to investigate the influence of different types of middle cerebral artery high-density signs on the prognosis of ischemic stroke. Brain topographic maps can directly reflect the neural activity of the brain, so we used them to detect changes in brain function and brain topological power response after stroke. Compared the MI group and control group after rehabilitation training, better functional outcome was observed after MI-BCI rehabilitation, including a significantly higher probability of achieving a relevant increase in the Total FMA scores (MI = 16.70 ± 12.79, control = 5.34 ± 10.48), FMA shoulder and elbow scores (MI = 12.56 ± 6.37, control = 2.45 ± 7.91), FMA wrist scores (MI = 11.01 ± 3.48, control = 3.36 ± 5.79), the MAS scores (MI = 3.62 ± 2.48, control = 1.85 ± 2.89), the NCCT (MI = 21.94 ± 2.37, control = 17.86 ± 3.55). The findings demonstrate that MI-BCI rehabilitation training could more effectively improve motor function after upper limb motor dysfunction after stroke compared with routine rehabilitation training, which verifies the feasibility of active induction of neural rehabilitation. The severity of the patient’s condition may affect the rehabilitation effect of the MI-BCI system.

EEG-Based Control of a 3D-Printed Upper Limb Exoskeleton for Stroke Rehabilitation

“…There was no difference in terms of movement performance among the volunteer but there was a disparity in the speed of the patient's adaptation to the exoskeleton, which was slightly slower compared to that of typical individuals. Referring to some studies in this field, in the study [19], there was an issue with the duration of the rehabilitation. The MI group utilized two rehabilitation procedures, leading to a longer rehabilitation period compared to the control group, which impacted the scoring.…”

Brain–Computer Interfaces for Upper Limb Motor Recovery after Stroke: Current Status and Development Prospects (Review)

“…The scientific justification for this method has been the data on the positive effect of the motor imagery process on neuroplasticity due to activation of motor structures of the central nervous system (CNS) [4][5][6][7][8]. By providing feedback during motor imagery, the BCI systems enhance the effectiveness of such training sessions [9]. In general, training with the use of the BCI technology in patients after stroke includes the following processes: a patient is asked to mentally perform a movement of the paralyzed limb; the BCI technology using non-invasive sensors records brain signals accompanying the mental performance of the task; in real time, these signals are recognized and converted into a control command for an external device; the patient is provided with reviews feedback on the quality of the mental task performance using the external device [10].…”

Research Progress of Human–Computer Interaction Technology Based on Gesture Recognition

“…Complex tasks can now be precisely accomplished by simply driving the computer with simple signals. As technology has advanced, researchers have also developed various human-machine interaction technologies, including voice control [1][2][3], brain-computer interfaces [4][5][6][7], facial expression control [8][9][10][11], and gesture recognition, among others, further enhancing the freedom, applicability, and efficiency of human-machine interaction.…”