Objective . We proposed a brain–computer interface (BCI) based visual-haptic neurofeedback training (NFT) by incorporating synchronous visual scene and proprioceptive electrical stimulation feedback. The goal of this work was to improve sensorimotor cortical activations and classification performance during motor imagery (MI). In addition, their correlations and brain network patterns were also investigated respectively. Approach . 64-channel electroencephalographic (EEG) data were recorded in nineteen healthy subjects during MI before and after NFT. During NFT sessions, the synchronous visual-haptic feedbacks were driven by real-time lateralized relative event-related desynchronization (lrERD). Main results . By comparison between previous and posterior control sessions, the cortical activations measured by multi-band (i.e. alpha_1: 8–10 Hz, alpha_2: 11–13 Hz, beta_1: 15–20 Hz and beta_2: 22–28 Hz) absolute ERD powers and lrERD patterns were significantly enhanced after the NFT. The classification performance was also significantly improved, achieving a ~9% improvement and reaching ~85% in mean classification accuracy from a relatively poor performance. Additionally, there were significant correlations between lrERD patterns and classification accuracies. The partial directed coherence based functional connectivity (FC) networks covering the sensorimotor area also showed an increase after the NFT. Significance . These findings validate the feasibility of our proposed NFT to improve sensorimotor cortical activations and BCI performance during motor imagery. And it is promising to optimize conventional NFT manner and evaluate the effectiveness of motor training.

Motor imagery (MI) induced EEG patterns are widely used as control signals for brain-computer interfaces (BCIs). Kinetic and kinematic factors have been proved to be able to change EEG patterns during motor execution and motor imagery. However, to our knowledge, there is still no literature reporting an effective online MI-BCI using kinetic factor regulated EEG oscillations. This study proposed a novel MI-BCI paradigm in which users can online output multiple commands by imagining clenching their right hand with different force loads. Eleven subjects participated in this study. During the experiment, they were asked to imagine clenching their right hands with two different force loads (30% maximum voluntary contraction (MVC) and 10% MVC). Multi-Common spatial patterns (Multi-CSPs) and support vector machines (SVMs) were used to build the classifier for recognizing three commands corresponding to high load MI, low load MI and relaxed status respectively. EMG were monitored to avoid voluntary muscle activities during the BCI operation. The event-related spectral perturbation (ERSP) method was used to analyse EEG variation during multiple load MI tasks. All subjects were able to drive BCI systems using motor imagery of different force loads in online experiments. We achieved an average online accuracy of 70.9%, with the highest accuracy of 83.3%, which was much higher than the chance level (33%). The event-related desynchronization (ERD) phenomenon during high load tasks was significantly higher than it was during low load tasks both in terms of intensity at electrode positions C3 (p < 0.05) and spatial distribution. This paper demonstrated the feasibility of the proposed MI-BCI paradigm based on multi-force loads on the same limb through online studies. This paradigm could not only enlarge the command set of MI-BCI, but also provide a promising approach to rehabilitate patients with motor disabilities.

Action planning is an important decision-making process, which can be specially affected by environment. Response selection during action planning has been demonstrated to be modulated by tVNS. Therefore, tVNS shows a great potential for modulating the action planning process. We aimed to explore the tVNS-induced effect on action planning in behavioural and electrophysiology. Twenty-eight participants were randomly divided into two groups (active group and sham group). A single-blind, sham-controlled between-subject design was applied to explore the effect of online-tVNS (i.e., tVNS overlapping with the task) on action planning paradigm. We measured and compared reaction time (RT) and movement-related cortical potentials (MRCPs) before and after tVNS between active and sham groups. As compared to sham group, for the ipsilateral hand/contralateral hemisphere relative to the stimulated side, active tVNS significantly reduced the reaction time and decreased the MRCP amplitude mainly in the challenging tasks. Our results indicate that tVNS can produce a lateralization effect on action planning, especially plays an important role in the more challenging tasks as reflected both in the behavioural and electrophysiological results.

Brain-computer interface (BCI)-based motor rehabilitation feedback training system can facilitate motor function reconstruction, but its rehabilitation mechanism with suitable training protocol is unclear, which affects the application effect. To this end, we probed the electroencephalographic (EEG) activations induced by motor imagery (MI) and action observation (AO) to provide an effective method to optimize motor feedback training. We grouped subjects according to their alpha–band sensorimotor cortical excitability under MI and AO conditions, and investigated the EEG response under the same paradigm between groups and different motor paradigms within group, respectively. The results showed that there were significant differences in sensorimotor activations between two groups of subjects. Specifically, the group with weaker MI induced EEG features, could achieve stronger sensorimotor activations in AO than that of other conditions. The group with stronger MI induced EEG features, could achieve stronger sensorimotor activations in the MI+AO than that of other conditions. We also explored their classification and brain network differences, which might try to explain the EEG mechanism in different individuals and help stroke patients to choose appropriate subject-specific motor training paradigm for their rehabilitation and better treatment outcomes.

Objective: Motor imagery (MI) based brain- computer interface (BCI) has been widely studied as an effective way to enhance motor learning and promote motor recovery. However, the accuracy of MI-BCI heavily depends on whether subjects can perform MI tasks correctly, which largely limits the general application of MI-BCI. To overcome this limitation, a training strategy based on the combination of MI and sensory threshold somatosensory electrical stimulation (MI+st-SES) is proposed in this study. Methods: Thirty healthy subjects were recruited and randomly divided into SES group and control group. Both groups performed left-hand and right-hand MI tasks in three consecutive blocks. The main difference between two groups lies in the second block, where subjects in SES group received the st-SES during MI tasks whereas the control group performed MI tasks only. Results: The results showed that the SES group had a significant improvement in event-related desynchronization (ERD) of alpha rhythm after the training session of MI+st-SES (left-hand: F(2,27) = 9.98, p <0.01; right-hand: F(2, 27) = 10.43, p <0.01). The classification accuracy between left- and right-hand MI in the SES group was also significantly improved following MI+st-SES training (F(2,27) = 6.46, p <0.01). In contrary, there was no significant difference between the first and third blocks in the control group (F(2,27) = 0.18, p = 0.84). The functional connectivity based on weighted pairwise phase consistency (wPPC) over the sensorimotor area also showed an increase after the MI+st-SES training. Conclusion and Significance: Our findings indicate that training based on MI+st-SES is a promising way to foster MI performance and assist subjects in achieving efficient BCI control.