Objective: Stroke patients often suffer from hand dysfunction or loss of tactile perception, which in turn interferes with hand rehabilitation. Tactile-enhanced multi-sensory feedback rehabilitation is an approach worth considering, but its effectiveness has not been well studied. By using functional near-infrared spectroscopy (fNIRS) to analyze the causal activity patterns in the sensorimotor cortex, the present study aims to investigate the cortical hemodynamic effects of hand rehabilitation training when tactile stimulation is applied, and to provide a basis for rehabilitation program development.

Methods: A vibrotactile enhanced pneumatically actuated hand rehabilitation device was tested on the less-preferred hand of 14 healthy right-handed subjects. The training tasks consisted of move hand and observe video (MO), move hand and vibration stimulation (MV), move hand, observe video, and vibration stimulation (MOV), and a contrast resting task. Region of interest (ROI), a laterality index (LI), and causal brain network analysis methods were used to explore the brain’s cortical blood flow response to a multi-sensory feedback rehabilitation task from multiple perspectives.

Results: (1) A more pronounced contralateral activation in the right-brain region occurred under the MOV stimulation. Rehabilitation tasks containing vibrotactile enhancement (MV and MOV) had significantly more oxyhemoglobin than the MO task at 5 s after the task starts, indicating faster contralateral activation in sensorimotor brain regions. (2) Five significant lateralized channel connections were generated under the MV and MOV tasks (p < 0.05), one significant lateralized channel connection was generated by the MO task, and the Rest were not, showing that MV and MOV caused stronger lateralization activation. (3) We investigated all thresholds of granger causality (GC) resulting in consistent relative numbers of effect connections. MV elicited stronger causal interactions between the left and right cerebral hemispheres, and at the GC threshold of 0.4, there were 13 causal network connection pairs for MV, 7 for MO, and 9 for MOV.

Conclusion: Vibrotactile cutaneous stimulation as a tactile enhancement can produce a stronger stimulation of the brain’s sensorimotor brain areas, promoting the establishment of neural pathways, and causing a richer effect between the left and right cerebral hemispheres. The combination of kinesthetic, vibrotactile, and visual stimulation can achieve a more prominent training efficiency from the perspective of functional cerebral hemodynamics.

Emotion recognition based on EEG (electroencephalogram) has become a research hotspot in the field of brain-computer interfaces (BCI). Compared with traditional machine learning, the convolutional neural network model has substantial advantages in automatic feature extraction in EEG-based emotion recognition. Motivated by the studies that multiple smaller scale kernels could increase non-linear expression than a larger scale, we propose a 3D convolutional neural network model with multiscale convolutional kernels to recognize emotional states based on EEG signals. We select more suitable time window data to carry out the emotion recognition of four classes (low valence vs. low arousal, low valence vs. high arousal, high valence vs. low arousal, and high valence vs. high arousal). The results using EEG signals in the DEAP and SEED-IV datasets show accuracies for our proposed emotion recognition network model (ERN) of 95.67 and 89.55%, respectively. The experimental results demonstrate that the proposed approach is potentially useful for enhancing emotional experience in BCI.