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

Wang, Hongtao; Li, Ting; Bezerianos, Anastasios; Huang, Hui; He, Yuebang; Chen, Peng

doi:10.1007/s11517-018-1883-3

Cited by 24 publications

(11 citation statements)

References 28 publications

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“…The information transfer rate (ITR) in the on-line experiment reaches 85.80 bits/min and the task success rate is 91.1%. Besides, a recently published paper designed a virtual automatic car based on multiple patterns of MI-based BCI (Wang et al, 2019). The participants were asked to navigate the virtual automatic car through the six predefined destinations sequentially.…”

Section: Discussionmentioning

confidence: 99%

“…In comparison with the abovementioned simulated systems (Long et al, 2012;Bi et al, 2014;Li et al, 2018;Wang et al, 2019), which aimed to verify and test the feasibility and performance of the designed BCW or BCI systems, the present study designed and applied an indoor simulated training environment to improve the performance of users in BCW control. As results of the individual accuracy task, the average accuracy is 93.55% and the maximum individual accuracy is 100.00% (see Table 1).…”

Section: Discussionmentioning

confidence: 99%

“…Through audio-cued MI-based BCI, Francisco et al (2013) achieved wheelchair control in virtual and real environments. Besides, Wang and coworkers also proposed multiple patterns of MI to implement the movement control of the virtual automatic car (Wang et al, 2019). In terms of the simulated systems based on SSVEP-based BCI or hybrid BCI, Bi et al (2014) applied SSVEP-based BCI to control a simulated vehicle and Li et al (2018) combined hybrid BCI with computer vision to build a simulated driving system.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…In the preprocessing, the spatial filtering of MI data adopts common average reference (CAR) [29]. In band pass filtering, we divided the frequency band into 2, 6, 10, 12 frequency bands with different ranges for band pass filtering.…”

Section: B Signal Preprocessingmentioning

confidence: 99%

An Approach of One-vs-Rest Filter Bank Common Spatial Pattern and Spiking Neural Networks for Multiple Motor Imagery Decoding

Wang

Tang

et al. 2020

IEEE Access

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Motor imagery (MI) is a typical BCI paradigm and has been widely applied into many aspects (e.g. brain-driven wheelchair and motor function rehabilitation training). Although significant achievements have been achieved, multiple motor imagery decoding is still unsatisfactory. To deal with this challenging issue, firstly, a segment of electroencephalogram was extracted and preprocessed. Secondly, we applied a filter bank common spatial pattern (FBCSP) with one-vs-rest (OVR) strategy to extract the spatio-temporal-frequency features of multiple MI. Thirdly, the F-score was employed to optimise and select these features. Finally, the optimized features were fed to the spiking neural networks (SNN) for classification. Evaluation was conducted on two public multiple MI datasets (Dataset IIIa of the BCI competition III and Dataset IIa of the BCI competition IV). Experimental results showed that the average accuracy of the proposed framework reached up to 90.09% (kappa: 0.868) and 81.33% (kappa: 0.751) on the two public datasets, respectively. The achieved performance (accuracy and kappa) was comparable to the best one of the compared methods. This study demonstrated that the proposed method can be used as an alternative approach for multiple MI decoding and it provided a potential solution for online multiple MI detection. INDEX TERMS Electroencephalogram, motor imagery (MI), filter bank common spatial pattern (FBCSP), spiking neural networks (SNN). I. INTRODUCTION Electroencephalography (EEG) signal is usually used in brain-computer interface (BCI) systems due to its high temporal resolution [1]. Motor imagery (MI)-based BCI is one of classical paradigms and has been employed to restore the communication pathway or movement function for disabled, paralyzed, and stroke patients [2], [3]. Patients are able to The associate editor coordinating the review of this manuscript and approving it for publication was Li He .

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“…The brain activity recorded via EEG can be classified depending on the frequency of the signal. In particular, the alpha activity (8)(9)(10)(11)(12) and the beta activity (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) are mostly related to MI [3]. In addition, the increase or decrease in the activity at a certain frequency band locked to the onset of the MI refer to the event-related synchronization and desynchronization (ERS/ERD), respectively [4].…”

Section: Introductionmentioning

confidence: 99%

Diverse Feature Blend Based on Filter-Bank Common Spatial Pattern and Brain Functional Connectivity for Multiple Motor Imagery Detection

Wang

Tang

et al. 2020

IEEE Access

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Motor imagery (MI) based brain-computer interface (BCI) is a research hotspot and has attracted lots of attention. Within this research topic, multiple MI classification is a challenge due to the difficulties caused by time-varying spatial features across different individuals. To deal with this challenge, we tried to fuse brain functional connectivity (BFC) and one-versus-the-rest filter-bank common spatial pattern (OVR-FBCSP) to improve the robustness of classification. The BFC features were extracted by phase locking value (PLV), representing the brain interregional interactions relevant to the MI, whilst the OVR-FBCSP is used to extract the spatial-frequency features related to the MI. These diverse features were then fed into a multi-kernel relevance vector machine (MK-RVM). The dataset with three motor imagery tasks (left hand MI, right hand MI, and feet MI) was used to assess the proposed method. Experimental results not only showed that the cascade structure of diverse feature fusion and MK-RVM achieved satisfactory classification performance (average accuracy: 83.81%, average kappa: 0.76), but also demonstrated that BFC plays a supplementary role in the MI classification. Moreover, the proposed method has a potential to be integrated into multiple MI online detection owing to the advantage of strong timeefficiency of RVM. INDEX TERMS Multiple motor imagery, filter-bank common spatial pattern (FBCSP), phase locking value (PLV), brain functional connectivity (BFC), multi-kernel relevance vector machine (MK-RVM).

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

Cited by 24 publications

References 28 publications

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

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

An Approach of One-vs-Rest Filter Bank Common Spatial Pattern and Spiking Neural Networks for Multiple Motor Imagery Decoding

Diverse Feature Blend Based on Filter-Bank Common Spatial Pattern and Brain Functional Connectivity for Multiple Motor Imagery Detection

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