Self-Paced Operation of a Wheelchair Based on a Hybrid Brain-Computer Interface Combining Motor Imagery and P300 Potential

Yu, Yang; Zhou, Zongtan; Liu, Yadong; Jiang, Jun; Yin, Erwei; Zhang, Nannan; Wang, Zhihua; Liu, Yaru; Wu, Xingjie; Hu, Dewen

doi:10.1109/tnsre.2017.2766365

Cited by 93 publications

(72 citation statements)

References 49 publications

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“…Among these articles, forty-four, three, and two articles introduced EEG-, NIRS-, and ECoG-based brain-switches, respectively. Most studies have used EEG for developing a brain-switch [8,33,35,40,51,52,54,55,[58][59][60][61][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80][81][82][83][84][86][87][88][89][90][91][92][93][94][95][96][97][98]. EEG-based brain-switches have been mostly developed based on endogenous paradigms, which use self-regulated brain activity through mental imagery tasks without the use of external stimuli (n = 35) [40,51,52,55,…”

Section: Experimental Paradigms To Evaluate the Performance Of Brain-mentioning

confidence: 99%

“…Through an experiment conducted on six healthy subjects, they showed a high TPR of 91.3% and a low FPR of 0.4 FPs/min. More recently, common spatial pattern (CSP) algorithms have been used to develop EEG-based endogenous brain-switches [40,77,97]. A CSP algorithm uses EEG signals filtered between 8 and 30 Hz containing MI-related brain activity information.…”

Section: Endogenous Brain-switchesmentioning

confidence: 99%

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Brain-Switches for Asynchronous Brain–Computer Interfaces: A Systematic Review

2020

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A brain–computer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever the user wants. However, the relatively low performance of an asynchronous BCI system is problematic because redundant BCI commands are required to correct false-positive operations. To significantly reduce the number of false-positive operations of an asynchronous BCI system, a two-step approach has been proposed using a brain-switch that first determines whether the user wants to use an asynchronous BCI system before the operation of the asynchronous BCI system. This study presents a systematic review of the state-of-the-art brain-switch techniques and future research directions. To this end, we reviewed brain-switch research articles published from 2000 to 2019 in terms of their (a) neuroimaging modality, (b) paradigm, (c) operation algorithm, and (d) performance.

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Section: Experimental Paradigms To Evaluate the Performance Of Brain-mentioning

confidence: 99%

Section: Endogenous Brain-switchesmentioning

confidence: 99%

Brain-Switches for Asynchronous Brain–Computer Interfaces: A Systematic Review

2020

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“…Rehabilitation using BCI is still not used in clinical practice [97]. "Go" and "Stop" movement P300 + SSVEP [54] Speed SSVEP + MI [65] Multi-degree SSVEP + MI [86] Speed and direction SSVEP + MI [63] P300 + MI [87,88] Autonomous navigation P300 + MI [89] Computer cursor 2-D SSVEP + MI [52,90] P300 + MI [72] Speller Spelling accuracy P300 + SSVEP [55,68] P300 + MI [71,73] Artificial limb Upper limb SSVEP + MI [62,64] Multidimensional robotic arm [91] Functional electrical therapy Advanced SSVEP + MI [66] ALS patients Communication P300 + MI [92] Awareness P300 + SSVEP [93] Virtual environment Smart home P300 + SSVEP [94,95] Intelligent nursing bed [96] in EEG acquisition due to electrodes placement, skull muscle movement, environment noises, limitations in hardware, and their calibrations. Two or more tasks need to be performed simultaneously in hybrid that might increase mental workload and cause discomfort to some users.…”

Section: Limitationsmentioning

confidence: 99%

Hybrid Brain-Computer Interface Systems: Approaches, Features, and Trends

Guragain¹,

Haider²,

Fazel-Rezai³

2018

Evolving BCI Therapy - Engaging Brain State Dynamics

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Brain-computer interface (BCI) is an emerging field, and an increasing number of BCI research projects are being carried globally to interface computer with human using EEG for useful operations in both healthy and locked persons. Although several methods have been used to enhance the BCI performance in terms of signal processing, noise reduction, accuracy, information transfer rate, and user acceptability, the effective BCI system is still in the verge of development. So far, various modifications on single BCI systems as well as hybrid are done and the hybrid BCIs have shown increased but insufficient performance. Therefore, more efficient hybrid BCI models are still under the investigation by different research groups. In this review chapter, single BCI systems are briefly discussed and more detail discussions on hybrid BCIs, their modifications, operations, and performances with comparisons in terms of signal processing approaches, applications, limitations, and future scopes are presented.

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“…Technically, the signals obtained from spontaneous or evoked brain activities are used to generate and send commands operating the wheelchair. The common types include event-related desynchronization (ERD)/eventrelated synchronization (ERS)-based BCW (Tanaka et al, 2005;Wang and Bezerianos, 2017), P300-based BCW (Iturrate et al, 2009;Rebsamen et al, 2010;Yu et al, 2017), steady-state somatosensory evoked potentials (SSSEP)-based BCW (Kim et al, 2018), steady-state visual evoked potentials (SSVEP)-based BCW (Diez et al, 2013;Müller et al, 2013) and hybrid BCW (Long et al, 2012;Cao et al, 2014;Li et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…However, for the development of BCW, the practical application is still a critical problem (Yu et al, 2017). It is difficult and, to some extent, dangerous for patients to control BCW in complex situations, especially for naive users (Bi et al, 2013;Fernández-Rodríguez et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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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Self-Paced Operation of a Wheelchair Based on a Hybrid Brain-Computer Interface Combining Motor Imagery and P300 Potential

Cited by 93 publications

References 49 publications

Brain-Switches for Asynchronous Brain–Computer Interfaces: A Systematic Review

Brain-Switches for Asynchronous Brain–Computer Interfaces: A Systematic Review

Hybrid Brain-Computer Interface Systems: Approaches, Features, and Trends

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

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