Effect of brain-computer interface training based on non-invasive electroencephalography using motor imagery on functional recovery after stroke - a systematic review and meta-analysis.

Background: Training with brain-computer interface (BCI) technology in the rehabilitation of patients after a stroke is rapidly developing. Numerous RCT investigated the effects of BCI training (BCIT) on recovery of motor and brain function in patients after stroke. Methods: A systematic literature search was performed in Medline, IEEE Xplore Digital Library, Cochrane library, and Embase in July 2018 and was repeated in March 2019. RCT or controlled clinical trials that included BCIT for improving motor and brain recovery in patients after a stroke were identified. Data were meta-analysed using the random-effects model. Standardized mean difference (SMD) with 95% confidence (95%CI) and 95% prediction interval (95%PI) were calculated. A meta-regression was performed to evaluate the effects of covariates on the pooled effect-size. Results: In total, 14 studies, including 362 patients after ischemic and hemorrhagic stroke (cortical, subcortical, 121 females; mean age 53.0+/- 5.8; mean time since stroke onset 15.7+/- 18.2 months) were included. Main motor recovery outcome measure used was the Fugl-Meyer Assessment. Quantitative analysis showed that a BCI training compared to conventional therapy alone in patients after stroke was effective with an SMD of 0.39 (95%CI: 0.17 to 0.62; 95%PI of 0.13 to 0.66) for motor function recovery of the upper extremity. An SMD of 0.41 (95%CI: - 0.29 to 1.12) for motor function recovery of the lower extremity was found. BCI training enhanced brain function recovery with an SMD of 1.11 (95%CI: 0.64 to 1.59; 95%PI ranging from 0.33 to 1.89). Covariates such as training duration, impairment level of the upper extremity, and the combination of both did not show significant effects on the overall pooled estimate. Conclusion: This meta-analysis showed evidence that BCI training added to conventional therapy may enhance motor functioning of the upper extremity and brain function recovery in patients after a stroke. We recommend a standardised evaluation of motor imagery ability of included patients and the assessment of brain function recovery should consider neuropsychological aspects (attention, concentration). Further influencing factors on motor recovery due to BCI technology might consider factors such as age, lesion type and location, quality of performance of motor imagery, or neuropsychological aspects

Clinically Significant Gains in Skillful Grasp Coordination by an Individual With Tetraplegia Using an Implanted Brain-Computer Interface With Forearm Transcutaneous Muscle Stimulation

© 2019 American Congress of Rehabilitation Medicine Objective: To demonstrate naturalistic motor control speed, coordinated grasp, and carryover from trained to novel objects by an individual with tetraplegia using a brain-computer interface (BCI)-controlled neuroprosthetic. Design: Phase I trial for an intracortical BCI integrated with forearm functional electrical stimulation (FES). Data reported span postimplant days 137 to 1478. Setting: Tertiary care outpatient rehabilitation center. Participant: A 27-year-old man with C5 class A (on the American Spinal Injury Association Impairment Scale) traumatic spinal cord injury Interventions: After array implantation in his left (dominant) motor cortex, the participant trained with BCI-FES to control dynamic, coordinated forearm, wrist, and hand movements. Main Outcome Measures: Performance on standardized tests of arm motor ability (Graded Redefined Assessment of Strength, Sensibility, and Prehension [GRASSP], Action Research Arm Test [ARAT], Grasp and Release Test [GRT], Box and Block Test), grip myometry, and functional activity measures (Capabilities of Upper Extremity Test [CUE-T], Quadriplegia Index of Function-Short Form [QIF-SF], Spinal Cord Independence Measure–Self-Report [SCIM-SR]) with and without the BCI-FES. Results: With BCI-FES, scores improved from baseline on the following: Grip force (2.9 kg); ARAT cup, cylinders, ball, bar, and blocks; GRT can, fork, peg, weight, and tape; GRASSP strength and prehension (unscrewing lids, pouring from a bottle, transferring pegs); and CUE-T wrist and hand skills. QIF-SF and SCIM-SR eating, grooming, and toileting activities were expected to improve with home use of BCI-FES. Pincer grips and mobility were unaffected. BCI-FES grip skills enabled the participant to play an adapted “Battleship” game and manipulate household objects. Conclusions: Using BCI-FES, the participant performed skillful and coordinated grasps and made clinically significant gains in tests of upper limb function. Practice generalized from training objects to household items and leisure activities. Motor ability improved for palmar, lateral, and tip-to-tip grips. The expects eventual home use to confer greater independence for activities of daily living, consistent with observed neurologic level gains from C5-6 to C7-T1. This marks a critical translational step toward clinical viability for BCI neuroprosthetics

Intracortical microstimulation of human somatosensory cortex as a source of cutaneous feedback

The field of brain computer interfaces (BCI) has been making rapid advances in decoding brain activity into control signals capable of operating neural prosthetic devices, such as dexterous robotic arms and computer cursors. Potential users of neural prostheses, including people with amputations or spinal cord injuries, retain intact brain function that can be decoded using BCIs. Recent work has demonstrated simultaneous control over up to 10 degrees-of-freedom, but the current paradigms lack a component crucial to normal motor control: somatosensory feedback. Currently, BCIs are controlled using visual feedback alone, which is important for many reaching movement and identifying target locations. However, as the actuators controlled by BCIs become more complex and include devices approximating the performance of human limbs, visual feedback becomes especially limiting, as it cannot convey information used during object manipulation, such as grip force. The objective of this work is to provide real-time, cutaneous, somatosensory feedback to users of dexterous prosthetic limbs under BCI control by applying intracortical microstimulation (ICMS) to primary somatosensory cortex (S1). Long-term microstimulation of the cortex with microelectrode arrays had never been attempted in a human prior to this work, and while this work is ultimately motivated by efforts to improve BCIs, this general approach also enables INTRACORTICAL MICROSTIMULATION OF HUMAN PRIMARY SOMATOSENSORY CORTEX AS A SOURCE OF CUTANEOUS FEEDBACK Sharlene Nicole Flesher, PhD University of Pittsburgh, 2017 v unprecedented access to the human cortex enabling investigations of more basic scientific issues surrounding cutaneous perception, its conscious components, and its role in motor planning and control. To this end, two microelectrode arrays were placed in human somatosensory cortex of a human participant. I first characterized qualities of sensations evoked via ICMS, such as percept location, modality, intensity and size, over a two-year study period. The sensations were found to be focal to a single digit, and increased in intensity linearly with pulse train amplitude, which suggests that ICMS will be a suitable means of relaying locations of object contact with single-digit precision, and a range of grasp forces can be relayed for each location. Additionally, I found these qualities to be stable over a two-year period, suggesting that delivering ICMS was not damaging the electrode-tissue interface. ICMS was then used as a real-time feedback source during BCI control of a robotic limb during tasks ranging from simple force-matching tasks to functional reach, grasp and carry tasks. Finally, we examined the relationship between pulse train parameters and conscious perception of sensations, an endeavor that until now could not have been undertaken. These results demonstrate that ICMS is a suitable means of relaying somatosensory feedback to BCI users. Adding somatosensory feedback to BCI users has the potential to improve embodiment and control of the devices, bringing this technology closer to restoring upper limb function