A hybrid brain computer interface system based on the neurophysiological protocol and brain-actuated switch for wheelchair control

Cao, Lei; Li, Jie; Ji, Hongfei

doi:10.1016/j.jneumeth.2014.03.011

Cited by 113 publications

(76 citation statements)

References 31 publications

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“…Although only a few relevant studies have appeared, they have successfully demonstrated the significance of the hybrid paradigm for control (Horki et al, 2011; Cao et al, 2014; Li et al, 2014) and rehabilitation applications (Daly et al, 2013; Yu et al, 2015). …”

Section: Combination Of Brain Signalsmentioning

confidence: 99%

“…The control of a wheelchair using MI and SSVEP has been proposed in two studies (Cao et al, 2014; Li et al, 2014). In the first case (Cao et al, 2014), MI was used for the chair’s left and right turning and SSVEP was used for the speed control (three commands).…”

Section: Combination Of Brain Signalsmentioning

confidence: 99%

“…In the first case (Cao et al, 2014), MI was used for the chair’s left and right turning and SSVEP was used for the speed control (three commands). An SVM-based classifier resulted in 90.6% of accurate control using the hybrid protocol.…”

Section: Combination Of Brain Signalsmentioning

confidence: 99%

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Hybrid Brain–Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review

2017

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In this article, non-invasive hybrid brain–computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain–computer interface (BCI) accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP) and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided.

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Section: Combination Of Brain Signalsmentioning

confidence: 99%

Section: Combination Of Brain Signalsmentioning

confidence: 99%

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Hybrid Brain–Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review

2017

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“…Subjects were able to achieve good performance. Cao et al (2014) provides eight commands to control the wheelchair (i.e. turn left, turn right, drive forward, accelerate, decelerate, drive at the uniform velocity, turn on and off the switch) using SSVEP and MI features simultaneously.…”

Section: Ssvep and Motor-imagerymentioning

confidence: 99%

The types of hybrid modalities in brain-computer interface systems: a review

Singla

Jatana

2017

IJBET

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Brain-Computer Interface (BCI) is a young research area for researchers. Increasing number of research activities improves several areas such as signal acquisition techniques, hardware development, machine learning, and signal processing and system integration. However, there are many disadvantages of conventional BCI approaches. For example, Motorimagery based BCIs requires extensive training of the subjects, P-300 based BCIs still requires several stimulus repetitions to obtain reliable accuracy and in SSVEP stimulus; number of commands is limited by the number of stimulus frequencies and many more. To overcome these disadvantages and further improvement in performance of the system, an increasing number of researchers have begun to explore hybrid BCI approaches, in which multiple BCI approaches are incorporated in a single BCI system. The purpose of this paper is to give a brief introduction to the different types of hybrid BCI techniques. There are many different types of hybrid BCI that can be used in a wide range of applications. The paradigm design plays a very important role in the performance of hybrid BCIs.

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“…The feature extraction methods used were quite heterogeneous; however, the power spectral density (PSD) can be highlighted as being the most used by the proposals with ERD/ERS (39) Path length, path optimality rate, time required, classification accuracy, wrong speed control time and collisions (49) Missions: to send a "go" command and keep the chair in place, both tasks for 30 seconds (53) Time required, useful and useless commands of switch control, selection time and collisions (54)…”

mentioning

confidence: 99%

Review of real brain-controlled wheelchairs

2016

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Abstract. This paper presents a review of the state of the art regarding wheelchairs driven by a brain-computer interface (BCI). Using a brain-controlled wheelchair (BCW), disabled users could handle a wheelchair through their brain activity, granting autonomy to move through an experimental environment. A classification is established, based on the characteristics of the BCW, such as the type of electroencephalographic (EEG) signal used, the navigation system employed by the wheelchair, the task for the participants, or the metrics used to evaluate the performance. Furthermore, these factors are compared according to the type of signal used, in order to clarify the differences among them. Finally, the trend of current research in this field is discussed, as well as the challenges that should be solved in the future.

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A hybrid brain computer interface system based on the neurophysiological protocol and brain-actuated switch for wheelchair control

Cited by 113 publications

References 31 publications

Hybrid Brain–Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review

Hybrid Brain–Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review

The types of hybrid modalities in brain-computer interface systems: a review

Review of real brain-controlled wheelchairs

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