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Background:Brain Computer Interfaces (BCI) pair brain activity with visual and/or motor feedback. They may provide sorely needed training alternatives for patients with severely impaired upper limb after stroke, where treatment choices are limited and unsatisfactory.Objectives:To examine if BCI-based training is more effective in improving upper limb (UL) motor function than conventional therapy in the subacute phase after stroke and if patients with preserved cortical-spinal tract (CST) integrity benefit more from BCI training.Methods:Forty patients with stroke and severe UL paresis (< 13 on Action Research Arm Test (ARAT)) were included and randomized to either BCI training as part of their rehabilitation or conventional UL rehabilitation with a target of 12 BCI sessions. The sessions were conducted 3-4 times weekly for 3-4 weeks. Main endpoint was the ARAT at three months post stroke. Secondary outcome measures comprised Fugl-Meyer Motor Assessment UL and Functional Independence Measure. At baseline, Transcranial direct stimulation (TMS) was performed to examine CST integrity. EEG data were analyzed to see if there were changes in Event-related desynchronization (ERD) and lateralization during the course of treatment.Results: Data from 35 patients were available at three months follow-up. In general, few patients (10/35) improved above the minimally clinically important difference of 6 points on ARAT, in the BCI group 5/15, in control 5/20. An independent samples Mann-Whitney U test revealed no differences between the groups, p=0.382. CST lesion (MEP-) was a significant predictor of poor outcome, p=ConclusionsThough more patients in the BCI group improved, the difference was not significant. Larger studies including only patients with some preserved CST integrity should be attempted.

When using EMG biofeedback to control the grasping force of a myoelectric prosthesis, subjects need to activate their muscles and maintain the myoelectric signal within an appropriate interval. However, their performance decreases for higher forces, because the myoelectric signal is more variable for stronger contractions. Therefore, the present study proposes to implement EMG biofeedback using nonlinear mapping, in which EMG intervals of increasing size are mapped to equal-sized intervals of the prosthesis velocity. To validate this approach, 20 able-bodied subjects performed force-matching tasks using Michelangelo prosthesis with and without EMG biofeedback with linear and nonlinear mapping. Additionally, four transradial amputees performed a functional task in the same feedback and mapping conditions. The success rate in producing desired force was significantly higher with feedback (65.4±15.9%) compared to no feedback (46.2±14.9%) as well as when using nonlinear (62.4±16.8%) versus linear mapping (49.2±17.2%). Overall, in able-bodied subjects, the highest success rate was obtained when EMG biofeedback was combined with nonlinear mapping (72%), and the opposite for linear mapping with no feedback (39.6%). The same trend was registered also in four amputee subjects. Therefore, EMG biofeedback improved prosthesis force control, especially when combined with nonlinear mapping, which showed to be an effective approach to counteract increasing variability of myoelectric signal for stronger contractions.

Enhancing the perception of the surrounding environment by conveying information through tactile stimulation can improve performance, especially when other senses are already overloaded (e.g., first responders). Delivering electrotactile messages using spatiotemporal patterns has been suggested as an efficient and reliable approach to transmitting information through the skin. However, the effectiveness of this approach depends on the user’s capability to recognize tactile messages in potentially challenging scenarios (e.g., rescue operations). Therefore, the present study evaluated the participants’ performance in identifying electrotactile messages while they performed a parallel cognitive task. Thirty-six electrotactile messages were delivered through a 3 × 2 pad-matrix at the lateral torso in the baseline and multitasking conditions. The task-switching paradigm was employed as the parallel task, introducing cognitive interference to the electrotactile message recognition. The assumption was that the simultaneous task would impair the identification performance, but that this impairment could be minimized by decreasing the speed at which the message was delivered. The results indeed showed that the identification success rates decreased from ~83% in the baseline to ~61% during multitasking. Unexpectedly, the performance did not recover when the message duration was increased. Hence, modulating the message duration seems not to be an effective strategy to prevent lower performance while multitasking.