MI-based BCI with accurate real-time three-class classification processing and light control application.

The use of brain-computer interfaces (BCIs) to control intelligent devices is a current and future research direction. However, the challenges of low accuracy of real-time recognition and the need for multiple electroencephalographic channels are yet to be overcome. While a number of research teams have proposed many ways to improve offline classification accuracy, the potential problems in real-time experiments are often overlooked. In this study, we proposed a label-based channel diversion preprocessing to solve the problem of low real-time classification accuracy. The Tikhonov regularised common spatial-pattern algorithm (TRCSP) and one vs rest support vector machine (OVR-SVM) were used for feature extraction and pattern classification. High accuracy was achieved in real-time three-class classification using only three channels (average real-time accuracy of 87.46%, with a maximum of 90.33%). In addition, the stability and reliability of the system were verified through lighting control experiments in a real environment. Using the autonomy of MI and real-time feedback of light brightness, we have built a fully autonomous interactive system. The improvement in the real-time classification accuracy in this study is of great significance to the industrialisation of BCI.

An overview of methods of left and right foot motor imagery based on Tikhonov regularisation common spatial pattern.

The motor imagery brain-computer interface (MI-BCI) provides an interactive control channel for spinal cord injury patients. However, the limitations of feature extraction algorithms may lead to low accuracy and instability in decoding electroencephalogram (EEG) signals. In this study, we examined the classification performance of an MI-BCI system by focusing on the distinction of the left and right foot kinaesthetic motor imagery tasks in five subjects. Feature extraction was performed using the common space pattern (CSP) and the Tikhonov regularisation CSP (TRCSP) spatial filters. TRCSP overcomes the CSP problems of noise sensitivity and overfitting. Moreover, support vector machine (SVM) and linear discriminant analysis (LDA) were used for classification and recognition. We constructed four combined classification methods (TRCSP-SVM, TRCSP-LDA, CSP-SVM, and CSP-LDA) and evaluated them by comparing their accuracies, kappa coefficients, and receiver operating characteristic (ROC) curves. The results showed that the TRCSP-SVM method performed significantly better than others (average accuracy 97%, average kappa coefficient 0.91, and average area under ROC curve (AUC) 0.98). Using TRCSP instead of standard CSP improved accuracy by up to 10%. This study provides insights into the classification of EEG signals. The results of this study can aid lower limb MI-BCI systems in rehabilitation training.

Control strategy for intraspinal microstimulation based on central pattern generator.

Intraspinal microstimulation (ISMS) is considered as a special functional electrical stimulation (FES) method. This method can restore the movement of paralyzed limbs in patients with spinal cord injury (SCI) using electrical stimulation of spinal cord. There is a special site for central pattern generator (CPG) in the spinal cord. The ISMS acts on the CPG site, and single electrode stimulation produces alternating motion in the hindlimbs of SCI rats. Based on the long short-term memory network (LSTM), a mapping model was established between the stimulation intensity of specific CPG sites and the angle of the knee joint to reflect the motor characteristics of the rat hindlimb. We proposed an LSTM-iterative learning control (ILC) strategy to form a closed-loop control to accurately control hindlimb movement. The proposed LSTM model fits the actual joint angle curve well, and the LSTM-ILC strategy can accurately regulate the hindlimb movement, allowing rats to perform rehabilitation training based on pre-set knee trajectories.

Central pattern generator network model for the alternating hind limb gait of rats based on the modified Van der Pol equation.

Herein, we employed a central pattern generator (CPG), a spinal cord neural network that regulates lower-limb gait during intra-spinal micro-stimulation (ISMS). Particularly, ISMS was used to determine the spatial distribution pattern of CPG sites in the spinal cord and the signal regulation pattern that induced the CPG network to produce coordinated actions. Based on the oscillation phenomenon of the single CPG neurons of Van der Pol (VDP) oscillators, a double-cell CPG neural network model was constructed to realise double lower limbs, six-joint modelling, the simulation of 12 neural circuits, the CPG loci characterising stimuli-inducing alternating movements and changes in polarity stimulation signals in rat hindlimbs, and leg-state change movements. The feasibility and effectiveness of the CPG neural network were verified by recording the electromyographic burst-release mode of the flexor and extensor muscles of the knee joints during CPG electrical stimulation. The results revealed that the output pattern of the CPG presented stable rhythm and coordination characteristics. The 12-neuron CPG model based on the improved VDP equation realised single-point control while significantly reducing the number of stimulation electrodes in the gait training of spinal cord injury patients. We believe that this study advances motor function recovery in rehabilitation medicine.

Gait regulation of hindlimb based on central pattern generator in rats with a spinal cord injury.

A spinal stimulator that can regulate hindlimb movements using monopolar stimulation has not been developed yet. Nevertheless, in a previous study, we found a specific central pattern generator site on the right side of the rat spinal cord. By stimulating these sites with certain pulse signals, the alternating movement of the hindlimb can be obtained using fewer electrodes. Therefore, in this research, considering the specific central pattern generator site as the target, functional electrical stimulation was performed on rats with spinal cord injury using monopolar stimulation. Angle sensors were used to track and capture the knee joint angle data of the right hindlimb; thus, the mapping relationship between the voltage amplitude and the knee angle parameters was established. Based on this relationship, the rats' hindlimb were controlled. Compared with the traditional spinal stimulator, the proposed approach increases the gait feedback, requires fewer electrodes, and simplifies the timing of stimulation. The rats with spinal cord injury were subjected to stimulation training for half an hour every day for 28 consecutive days. The Basso, Beattie and Bresnahan score showed that 76% of the health level could be achieved on the 28th day. Finally, somatosensory evoked potential analysis showed that the measurement results were close to the standard value on the 28th day. This study lays a foundation for future rehabilitation research on the hindlimb.