In code-modulated visual evoked potential (c-VEP) based BCI systems, flickering visual stimuli may result in visual fatigue. Thus, we introduced a discrete-interval binary sequence (DIBS) as visual stimulus modulation, with its power spectrum optimized to emphasize high-frequency components (40 Hz-60 Hz). 8 and 17 subjects participated, respectively, in offline and online experiments on a 4-target asynchronous c-VEP-based BCI system designed to realize a high positive predictive value (PPV), a low false positive rate (FPR) during idle states, and a high true positive rate (TPR) in control states, while minimizing visual fatigue level. Two visual stimuli modulations were introduced and compared: a maximum length sequence (m-sequence) and the high-frequency discrete-interval binary sequence (DIBS). The decoding algorithm was compared among the canonical correlation analysis (CCA), the task-related component analysis (TRCA), and two approaches of sub-band component weight calculation (the traditional method and the proportional method) for FBCCA and FBTRCA. In the online experiments, the average PPV, FPR and TPR achieved, respectively [Formula: see text], [Formula: see text], [Formula: see text] with m-sequence, while [Formula: see text], [Formula: see text] and [Formula: see text] with DIBS. Estimated by objective eye-related metrics and a subjective questionnaire, the visual fatigue in DIBS cases is significantly smaller than that in m-sequence cases. In this study, the feasibility of a novel modulation approach for visual fatigue reduction was proved in an asynchronous c-VEP system, while maintaining comparable performance to existing methods, which provides further insights towards enhancing this field's long-term viability and user-friendliness.

In order to reduce the visual fatigue during use, a high-frequency discrete-interval binary sequence (DIBS) was proposed for an asynchronous 4-target code-modulated visual evoked potential (c-VEP) brain-computer interface system. However, with traditional spatial filter-based decoding methods, some of the subjects have difficulties activating the high-frequency system from the idle states, which indicates that the system's effectiveness declined because of the user-specificity. A deep neural network was therefore built, consisting of two-way LSTM networks, for enhancing the decoding performances in the high-frequency stimulus cases. The architecture includes a pretraining phase with contrastive learning, a training phase and a fine-tuning phase. In the pseudo-online experiments, compared to the results of the usual filter-bank task-related component analysis (FB-TRCA) method, the invalid trial percentage was reduced from 22% to zero, the average reaction time (RT) from 2.75 s to 1.39 s, and the average false positive rate (FPR) from 8.33 × 10–2min–1to 1.39 × 10–2min–1by using the designed architecture. In this study, the detection of high-frequency c-VEP has been greatly improved, and the response signals were proved to contain useful information. All of the subjects were able to activate the system, which also verified the feasibility of high-frequency stimuli in other related experiments in the domain.

Brain-computer interface (BCI) provides a novel technology for patients and healthy human subjects to control a robotic arm. Currently, BCI control of a robotic arm to complete the reaching and grasping tasks in an unstructured environment is still challenging because the current BCI technology does not meet the requirement of manipulating a multi-degree robotic arm accurately and robustly. BCI based on steady-state visual evoked potential (SSVEP) could output a high information transfer rate; however, the conventional SSVEP paradigm failed to control a robotic arm to move continuously and accurately because the users have to switch their gaze between the flickering stimuli and the target frequently. This study proposed a novel SSVEP paradigm in which the flickering stimuli were attached to the robotic arm's gripper and moved with it. First, an offline experiment was designed to investigate the effects of moving flickering stimuli on the SSVEP's responses and decoding accuracy. After that, contrast experiments were conducted, and twelve subjects were recruited to participate in a robotic arm control experiment using both the paradigm one (P1, with moving flickering stimuli) and the paradigm two (P2, conventional fixed flickering stimuli) using a block randomization design to balance their sequences. Double blinks were used to trigger the grasping action asynchronously whenever the subjects were confident that the position of the robotic arm's gripper was accurate enough. Experimental results showed that the paradigm P1 with moving flickering stimuli provided a much better control performance than the conventional paradigm P2 in completing a reaching and grasping task in an unstructured environment. Subjects' subjective feedback scored by a NASA-TLX mental workload scale also corroborated the BCI control performance. The results of this study suggest that the proposed control interface based on SSVEP BCI provides a better solution for robotic arm control to complete the accurate reaching and grasping tasks.

Ultrasound can non-invasively detect muscle deformations and has great potential applications in prosthetic hand control. Traditional ultrasound equipment was usually too bulky to be applied in wearable scenarios. This research presented a compact ultrasound device that could be integrated into a prosthetic hand socket. The miniaturized ultrasound system included four A-mode ultrasound transducers for sensing musculature deformations, a signal excitation/acquisition module, and a prosthetic hand control module. The size of the ultrasound system was 65*75*25 mm, weighing only 85 g. For the first time, we integrated the ultrasound system into a prosthetic hand socket to evaluate its performance in practical prosthetic hand control. We designed an experiment requiring twenty subjects to perform six commonly used gestures. The performance of decoding ultrasound signals was analyzed offline using four classification algorithms and then was assessed in online control. The average values of online classification accuracy with and without wearing the physical prosthetic were 91.5 [Formula: see text] and 96.5 [Formula: see text], respectively. We found that wearing the prosthetic hand influenced the ultrasound gestures classification accuracy, but remarkable online classification performance could still be maintained. These experimental results demonstrated the efficacy of the designed integrated ultrasound system for practical use, paving the way for an effective HMI system that could be widely used in prosthetic hand control.

The automatic train operation which integrates knowledge-based intelligent algorithm to develop safe and efficient control system has become one of the most important developing directions in the field of railway transit equipment. Multi-objective optimization is a strictly incompatible problem, and such contradiction is one of the main reasons that lead to the best multi-objective optimization difficult to achieve. In this paper, the multi-objective optimization feature information is transformed into the association function first, and then the matter-element theory is introduced to establish models for the speed trajectory to achieve the multi-objective optimization to fuse knowledge-based safety requirement constrained condition. Performance indices weighting of different performance in different stages are determined with the Hierarchical Mahalanobis distance method, and the decision speeds are calculated with goodness evaluation method. Taking Shanghai Railway Transit Equipment in China as a case study, this paper selected the multi-objective performance indices including passenger comfort, running stability, energy efficiency, and parking accuracy as objectives to support the decision-making. The multi-objective performance indices are evaluated by a field investigation and simulation. The test result shows that the comfort level, running stability, energy saving property, and parking accuracy are better than those derived by the traditional control algorithm. It indicates that the model has the advantage that it conveniently quantifies the qualitative indices, and it can integrate the data source information to improve the multi-objective performance indices, so that it is very useful to apply multi-source data and prior knowledge to multi-objective optimization of the automatic train operation control system.

One problem in the development of brain-computer interface (BCI) systems is to minimize the amount of subject training on the premise of accurate classification. Hence, the challenge is how to train the BCI system effectively especially in the scenario with small amount of training data. In this paper, we introduce improved semisupervised adaptation based on common spatial pattern (CSP) features. The feature extraction and classification are performed jointly and iteratively. In the iteration step, training data are expanded by part of the testing data with labels which are predicted by a linear discriminant analysis classifier and/or a Bayesian linear discriminant analysis classifier in the previous iteration. Then CSP features are reextracted from the expanded training data, and the classifiers are retrained. Both self-training and cotraining paradigms are proposed for the improved semisupervised adaptation. Throughout the investigation on different number of initial training trials, we find that when a small number of training trials are used, e.g., a training session contains no more than 30 trials, similar classification performance to that of large training data items (40-50 trials) can be achieved. Effectiveness of the algorithms is verified by two competition datasets. Compared with several existing algorithms, the proposed semisupervised algorithms show improvements in classification accuracy for most of the competition datasets especially in the case of small training data.