This paper presents a hybrid brain-computer interface (BCI) that combines motor imagery (MI) and P300 potential for the asynchronous operation of a brain-controlled wheelchair whose design is based on a Mecanum wheel. This paradigm is completely user-centric. By sequentially performing MI tasks or paying attention to P300 flashing, the user can use eleven functions to control the wheelchair: move forward/backward, move left/right, move left45/right45, accelerate/decelerate, turn left/right, and stop. The practicality and effectiveness of the proposed approach were validated in eight subjects, all of whom achieved good performance. The preliminary results indicated that the proposed hybrid BCI system with different mental strategies operating sequentially is feasible and has potential applications for practical self-paced control.

In this study, we presented a novel asynchronous speller for Chinese sinogram input by incorporating electroen-cephalography (EOG) into the conventional electroencephalog-raphy (EEG)-based spelling paradigm. An EOG-based brain switch was used to activate a classic row-column P300-based speller only when spelling was needed, enabling asynchronous op-eration of the system. Then, the user could input sinograms by al-ternately performing P300 and double-blink tasks until he or she intended to stop spelling. With the incorporation of an EOG de-tector, the system achieved rapid sinogram input. In addition to asynchronous operation, the performance of the proposed speller was compared with that achieved by a P300-based method alone across eighteen subjects. The proposed system showed a mean communication speed of approximately 2.39 sinograms per minute, an increase of 0.83 sinograms per minute compared with the P300-based method. The preliminary online performance indicated that the proposed paradigm is a very promising approach for practical Chinese sinogram input application. This system may also be ex-panded to users whose languages are written in logographic scripts to serve as an assistive communication tool.

In this paper, an asynchronous control paradigm based on sequential motor imagery (sMI) is proposed to enrich the control commands of a MI-based brain-computer interface (BCI). We test the feasibility and report the performance of this para-digm in wheelchair navigation control. By sequentially imaging left- and right-hand movements, the subjects can complete four sMI tasks in an asynchronous mode, which are then encoded to control six steering functions of a wheelchair, including moving forward, turning left, turning right, accelerating, decelerating and stopping. Two experiments, a simulated experiment and an online wheelchair navigation experiment, were conducted to evaluate the performance of the proposed approach in seven subjects. In summary, the subjects completed 99 of 105 experimental trials along a predefine route. The success rate was 94.2%, indicating the practicality and effectiveness of the proposed asynchronous control paradigm in wheelchair navigation control.

Cognitive load prediction is one of the most important issues in the nascent field of neuroergonomics, and it has significant value in real-world applications. Most of the previous studies of cognitive load prediction only utilized electroencephalography (EEG)-based spectral signatures or interchannel connectivity, ignoring abundant temporal microstate features, which may represent the transient topologies of EEG signals. Furthermore, previous studies have mostly focused on the binary-level classification of cognitive load for single-type cognitive tasks. To date, there are few studies on the multilevel prediction of cognitive load during mixed cognitive tasks. Here, we first designed a new paradigm termed the “finding fault game,” mixing multiple tasks of memory, counting, and visual search, and then developed a multidimensional analysis framework to improve cognitive load prediction using a fusion of spatial, temporal, and spectral EEG features. Specifically, EEG-based functional connectivity, microstates and power spectral densities (PSD) were calculated for three cognitive load levels. Twelve adult subjects participated in the study. The experimental results show that increased cognitive load was associated with elevated theta and degraded alpha power and significant changes in interchannel connectivity and microstates, and that fusing the three types of EEG features improved the performance of three-level cognitive load prediction, achieving the accuracies of greater than 80% in the cross-validation, real-time, and over-time prediction. The findings suggest that all three types of EEG features can serve as signatures of cognitive load and that their fusion can improve multilevel prediction.

Brain-controlled wheelchairs are one of the most promising applications that can help people gain mobility after their normal interaction pathways have been compromised by neuromuscular diseases. The feasibility of using brain signals to control wheelchairs has been well demonstrated by healthy people in previous studies. However, most potential users of brain-controlled wheelchairs are people suffering from severe physical disabilities or who are in a “locked-in” state. To further validate the clinical practicability of our previously proposed P300-based brain-controlled wheelchair, in this study, 10 subjects with severe spinal cord injuries participated in three experiments and completed ten predefined tasks in each experiment. The average accuracy and information transfer rate (ITR) were 94.8% and 4.2 bits/min, respectively. Moreover, we evaluated the physiological and cognitive burdens experienced by these individuals before and after the experiments. There were no significant changes in vital signs during the experiment, indicating minimal physiological and cognitive burden. The patients’ average systolic blood pressure before and after the experiment was 113±13.7 mmHg and 114±11.9 mmHg, respectively (P = 0.122). The patients’ average heart rates before and after the experiment were 79±8.4/min and 79±8.2/min, respectively (P = 0.147). The average task load, measured by the National Aeronautics and Space Administration task load index, ranged from 10.0 to 25.5. The results suggest that the proposed P300-based brain-controlled wheelchair is safe and reliable; additionally, it does not significantly increase the patient’s physical and mental task burden, demonstrating its potential value in clinical applications. Our study promotes the development of a more practical brain-controlled wheelchair system.

Predicting cognitive load is a crucial issue in the emerging field of human-computer interaction and holds significant practical value, particularly in flight scenarios. Although previous studies have realized efficient cognitive load classification, new research is still needed to adapt the current state-of-the-art multimodal fusion methods. Here, we proposed a feature selection framework based on multiview learning to address the challenges of information redundancy and reveal the common physiological mechanisms underlying cognitive load. Specifically, the multimodal signal features (EEG, EDA, ECG, EOG, & eye movements) at three cognitive load levels were estimated during multiattribute task battery (MATB) tasks performed by 22 healthy participants and fed into a feature selection-multiview classification with cohesion and diversity (FS-MCCD) framework. The optimized feature set was extracted from the original feature set by integrating the weight of each view and the feature weights to formulate the ranking criteria. The cognitive load prediction model, evaluated using real-time classification results, achieved an average accuracy of 81.08% and an average F1-score of 80.94% for three-class classification among 22 participants. Furthermore, the weights of the physiological signal features revealed the physiological mechanisms related to cognitive load. Specifically, heightened cognitive load was linked to amplified δ and θ power in the frontal lobe, reduced α power in the parietal lobe, and an increase in pupil diameter. Thus, the proposed multimodal feature fusion framework emphasizes the effectiveness and efficiency of using these features to predict cognitive load.