Traditional rehabilitation strategies become difficult in the chronic phase stage of stroke prognosis. Brain–computer interface (BCI) combined with external devices may improve motor function in chronic stroke patients, but it lacks comprehensive assessments of neurological changes regarding functional rehabilitation. This study aimed to comprehensively and quantitatively investigate the changes in brain activity induced by BCI–FES training in patients with chronic stroke. We analyzed the EEG of two groups of patients with chronic stroke, one group received functional electrical stimulation (FES) rehabilitation training (FES group) and the other group received BCI combined with FES training (BCI–FES group). We constructed functional networks in both groups of patients based on direct directed transfer function (dDTF) and assessed the changes in brain activity using graph theory analysis. The results of this study can be summarized as follows: (i) after rehabilitation training, the Fugl–Meyer assessment scale (FMA) score was significantly improved in the BCI–FES group (p < 0.05), and there was no significant difference in the FES group. (ii) Both the global and local graph theory measures of the brain network of patients with chronic stroke in the BCI–FES group were improved after rehabilitation training. (iii) The node strength in the contralesional hemisphere and central region of patients in the BCI–FES group was significantly higher than that in the FES group after the intervention (p < 0.05), and a significant increase in the node strength of C4 in the contralesional sensorimotor cortex region could be observed in the BCI–FES group (p < 0.05). These results suggest that BCI–FES rehabilitation training can induce clinically significant improvements in motor function of patients with chronic stroke. It can improve the functional integration and functional separation of brain networks and boost compensatory activity in the contralesional hemisphere to a certain extent. The findings of our study may provide new insights into understanding the plastic changes of brain activity in patients with chronic stroke induced by BCI–FES rehabilitation training.

PDMS (polydimethylsiloxane) is an important soft biocompatible material, which has various applications such as an implantable neural interface, a microfluidic chip, a wearable brain–computer interface, etc. However, the selective removal of the PDMS encapsulation layer is still a big challenge due to its chemical inertness and soft mechanical properties. Here, we use an excimer laser as a cold micro-machining tool for the precise removal of the PDMS encapsulation layer which can expose the electrode sites in an implantable neural interface. This study investigated and optimized the effect of excimer laser cutting parameters on the electrochemical impedance of a neural electrode by using orthogonal experiment design. Electrochemical impedance at the representative frequencies is discussed, which helps to construct the equivalent circuit model. Furthermore, the parameters of the equivalent circuit model are fitted, which reveals details about the electrochemical property of neural electrode using PDMS as an encapsulation layer. Our experimental findings suggest the promising application of excimer lasers in the micro-machining of implantable neural interface.

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Objective. The brain-computer interface system based on sensorimotor rhythm can convert the human spirit into instructions for machine control, and it is a new human-computer interaction system with broad applications. However, the spatial resolution of scalp EEG is limited due to the presence of volume conduction effects. Therefore, it is very meaningful to explore intracranial activities in a noninvasive way and improve the spatial resolution of EEG. Meanwhile, low-delay decoding is an essential factor for the development of a real-time BCI system. Approach. In this paper, EEG conduction is modelled by using public head anatomical templates, and cortical EEG is obtained using dynamic parameter statistical mapping. To solve the problem of a large amount of computation caused by the increase in the number of channels, the filter bank common spatial pattern method is used to obtain a spatial filter kernel, which reduces the computational cost of feature extraction to a linear level. And the feature classification and selection of important features are completed using a neural network containing band-spatial-time domain self-attention mechanisms. Main results. The results show that the method proposed in this paper achieves high accuracy for the four types of motor imagery EEG classification tasks, with fairly low latency and high physiological interpretability. Significance. The proposed decoding framework facilitates the realization of low-latency human-computer interaction systems.

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