The control of a virtual automatic car based on multiple patterns of motor imagery BCI

Decoding motor imagery loaded on steady-state somatosensory evoked potential based on complex task-related component analysis

BACKGROUND AND OBJECTIVE

Motor Imagery (MI) recognition is one of the most critical decoding problems in brain- computer interface field. Combined with the steady-state somatosensory evoked potential (MI-SSSEP), this new paradigm can achieve higher recognition accuracy than the traditional MI paradigm. Typical algorithms do not fully consider the characteristics of MI-SSSEP signals. Developing an algorithm that fully captures the paradigm's characteristics to reduce false triggering rate is the new step in improving performance.

METHODS

The idea to use complex signal task-related component analysis (cTRCA) algorithm for spatial filtering processing has been proposed in this paper according to the features of SSSEP signal. In this research, it's proved from the analysis of simulation signals that task-related component analysis (TRCA) as typical method is affected when the response between stimuli has reduced correlation and the proposed algorithm can effectively overcome this problem. The experimental data under the MI-SSSEP paradigm have been used to identify right-handed target tasks and three unique interference tasks are used to test the false triggering rate. cTRCA demonstrates superior performance as confirmed by the Wilcoxon signed-rank test.

RESULTS

The recognition algorithm of cTRCA combined with mutual information-based best individual feature (MIBIF) and minimum distance to mean (MDM) can obtain AUC value up to 0.89, which is much higher than traditional algorithm common spatial pattern (CSP) combined with support vector machine (SVM) (the average AUC value is 0.77, p < 0.05). Compared to CSP+SVM, this algorithm model reduced the false triggering rate from 38.69 % to 20.74 % (p < 0.001).

CONCLUSIONS

The research prove that TRCA is influenced by MI-SSSEP signals. The results further prove that the motor imagery task in the new paradigm MI-SSSEP causes the phase change in evoked potential. and the cTRCA algorithm based on such phase change is more suitable for this hybrid paradigm and more conducive to decoding the motor imagery task and reducing false triggering rate.

Improving performance in motor imagery BCI-based control applications via virtually embodied feedback

OBJECTIVE

Brain-computer interfaces (BCIs) based on motor imagery (MI) are commonly used for control applications. However, these applications require strong and discriminant neural patterns for which extensive experience in MI may be necessary. Inspired by the field of rehabilitation where embodiment is a key element for improving cortical activity, our study proposes a novel control scheme in which virtually embodiable feedback is provided during control to enhance performance.

METHODS

Subjects underwent two immersive virtual reality control scenarios in which they controlled the two-dimensional movement of a device using electroencephalography (EEG). The two scenarios only differ on whether embodiable feedback, which mirrors the movement of the classified intention, is provided. After undergoing each scenario, subjects also answered a questionnaire in which they rated how immersive the scenario and embodiable the feedback were.

RESULTS

Subjects exhibited higher control performance, greater discriminability in brain activity patterns, and enhanced cortical activation when using our control scheme compared to the standard control scheme in which embodiable feedback is absent. Moreover, the self-rated embodiment and presence scores showed significantly positive linear relationships with performance.

SIGNIFICANCE

The findings in our study provide evidence that providing embodiable feedback as guidance on how intention is classified may be effective for control applications by inducing enhanced neural activity and patterns with greater discriminability. By applying embodiable feedback to immersive virtual reality, our study also serves as another instance in which virtual reality is shown to be a promising tool for improving MI.

Indoor Simulated Training Environment for Brain-Controlled Wheelchair Based on Steady-State Visual Evoked Potentials

Brain-controlled wheelchair (BCW) has the potential to improve the quality of life for people with motor disabilities. A lot of training is necessary for users to learn and improve BCW control ability and the performances of BCW control are crucial for patients in daily use. In consideration of safety and efficiency, an indoor simulated training environment is built up in this paper to improve the performance of BCW control. The indoor simulated environment mainly realizes BCW implementation, simulated training scenario setup, path planning and recommendation, simulated operation, and scoring. And the BCW is based on steady-state visual evoked potentials (SSVEP) and the filter bank canonical correlation analysis (FBCCA) is used to analyze the electroencephalography (EEG). Five tasks include individual accuracy, simple linear path, obstacles avoidance, comprehensive steering scenarios, and evaluation task are designed, 10 healthy subjects were recruited and carried out the 7-days training experiment to assess the performance of the training environment. Scoring and command-consuming are conducted to evaluate the improvement before and after training. The results indicate that the average accuracy is 93.55% and improves from 91.05% in the first stage to 96.05% in the second stage (p = 0.001). Meanwhile, the average score increases from 79.88 in the first session to 96.66 in the last session and tend to be stable (p < 0.001). The average number of commands and collisions to complete the tasks decreases significantly with or without the approximate shortest path (p < 0.001). These results show that the performance of subjects in BCW control achieves improvement and verify the feasibility and effectiveness of the proposed simulated training environment.

Eye-controlled, power wheelchair performs well for ALS patients

BACKGROUND

Our pilot study tested the feasibility and performance of an eye-controlled power wheelchair for amyotrophic lateral sclerosis (ALS) patients.

METHODS

In this prospective pilot study, participants drove the wheelchair three times around an indoor course. We assessed the time to complete the course; starting and stopping on command; turning 90, 135, and 180 degrees; time to backup; and obstacle negotiation. Following their use of the wheelchair, subjects were given a questionnaire to assess user experience.

RESULTS

Twelve patients participated, and all were able to complete three trials without difficulty. Eight participants completed all of the individual tasks (eg, turning, stopping, etc.) without any errors. Overall performance ratings were high across all participants (4.6/5-excellent).

CONCLUSIONS

Our eye-controlled power wheelchair prototype is feasible and has a very favorable user experience. This system has the potential to improve the mobility and independence of ALS patients, and other groups with motor impairments.