A Self-Paced Motor Imagery Based Brain-Computer Interface for Robotic Wheelchair Control

Tsui, Chun Sing Louis; Gan, John Q.; Hu, Huosheng

doi:10.1177/155005941104200407

Cited by 61 publications

(47 citation statements)

References 16 publications

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“…From those studies based on the discrimination of mental tasks for controlling a wheelchair, only one provides 4 navigation commands [35] (move forward, turn left, turn right and stop) however, each command is associated to a specific motor imagery task, being necessary to discriminate between four different mental tasks. In [19, 21, 36] three mental tasks are discriminated in order to execute three different commands (move forward, turn left and turn right [19, 36] or turn left, turn right and stop [21]). Finally, in [9, 37, 38], the discrimination between two mental tasks provide only two different commands (turn left and turn right [9, 38] or turn right and move forward [37]).…”

Section: Discussionmentioning

confidence: 99%

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Brain-Computer Interface application: auditory serial interface to control a two-class motor-imagery-based wheelchair

Ron-Angevín

Velasco-Álvarez

Fernández-Rodríguez

et al. 2017

J NeuroEngineering Rehabil

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BackgroundCertain diseases affect brain areas that control the movements of the patients’ body, thereby limiting their autonomy and communication capacity. Research in the field of Brain-Computer Interfaces aims to provide patients with an alternative communication channel not based on muscular activity, but on the processing of brain signals. Through these systems, subjects can control external devices such as spellers to communicate, robotic prostheses to restore limb movements, or domotic systems. The present work focus on the non-muscular control of a robotic wheelchair.MethodA proposal to control a wheelchair through a Brain–Computer Interface based on the discrimination of only two mental tasks is presented in this study. The wheelchair displacement is performed with discrete movements. The control signals used are sensorimotor rhythms modulated through a right-hand motor imagery task or mental idle state. The peculiarity of the control system is that it is based on a serial auditory interface that provides the user with four navigation commands. The use of two mental tasks to select commands may facilitate control and reduce error rates compared to other endogenous control systems for wheelchairs.ResultsSeventeen subjects initially participated in the study; nine of them completed the three sessions of the proposed protocol. After the first calibration session, seven subjects were discarded due to a low control of their electroencephalographic signals; nine out of ten subjects controlled a virtual wheelchair during the second session; these same nine subjects achieved a medium accuracy level above 0.83 on the real wheelchair control session.ConclusionThe results suggest that more extensive training with the proposed control system can be an effective and safe option that will allow the displacement of a wheelchair in a controlled environment for potential users suffering from some types of motor neuron diseases.

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Section: Discussionmentioning

confidence: 99%

“…[19, 20]) or discrete ones (e.g. [9, 11, 21]). In the first case, the user controls the extension of the movements.…”

Section: Introductionmentioning

confidence: 99%

Brain-Computer Interface application: auditory serial interface to control a two-class motor-imagery-based wheelchair

Ron-Angevín

Velasco-Álvarez

Fernández-Rodríguez

et al. 2017

J NeuroEngineering Rehabil

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“…Five BCWs based on ERD/ERS signal had a low-level navigation system (18,34,43,44,57), three used a shared management (28, 35,46) and one with a highlevel navigation system (59). Therefore, it can be seen that the handling of low-level is used to a greater degree than shared control or highlevel.…”

mentioning

confidence: 93%

“…Other papers used methods such as learning vector quantization in mu and beta bands (43), the logarithmic value in the bands of interest (34,35), the common spatial patterns (CSP) (44,59) …”

Section: Muscle-assisted (36)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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“…Of particular interest would be a BMI system, based on the ERD/ERS of the sensorimotor area, able to control an assistive robot (downward arrow of Figure 1). Similarly, Tsui et al (2011); Huang et al (2012) have succeeded in controlling a wheelchair by means of a BMI based on event-related (de)synchronization (ERD/ERS). Nonetheless, one of the differences between a wheelchair and a lower limb assistive robot is that the latter induces movements of the legs.…”

Section: Introductionmentioning

confidence: 99%

Decoding the ERD/ERS: influence of afferent input induced by a leg assistive robot

Lisi

Noda²,

Morimoto³

2014

Front. Syst. Neurosci.

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This paper investigates the influence of the leg afferent input, induced by a leg assistive robot, on the decoding performance of a BMI system. Specifically, it focuses on a decoder based on the event-related (de)synchronization (ERD/ERS) of the sensorimotor area. The EEG experiment, performed with healthy subjects, is structured as a 3 × 2 factorial design, consisting of two factors: “finger tapping task” and “leg condition.” The former is divided into three levels (BMI classes), being left hand finger tapping, right hand finger tapping and no movement (Idle); while the latter is composed by two levels: leg perturbed (Pert) and leg not perturbed (NoPert). Specifically, the subjects' leg was periodically perturbed by an assistive robot in 5 out of 10 sessions of the experiment and not moved in the remaining sessions. The aim of this study is to verify that the decoding performance of the finger tapping task is comparable between the two conditions NoPert and Pert. Accordingly, a classifier is trained to output the class of the finger tapping, given as input the features associated with the ERD/ERS. Individually for each subject, the decoding performance is statistically compared between the NoPert and Pert conditions. Results show that the decoding performance is notably above chance, for all the subjects, under both conditions. Moreover, the statistical comparison do not highlight a significant difference between NoPert and Pert in any subject, which is confirmed by feature visualization.

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A Self-Paced Motor Imagery Based Brain-Computer Interface for Robotic Wheelchair Control

Cited by 61 publications

References 16 publications

Brain-Computer Interface application: auditory serial interface to control a two-class motor-imagery-based wheelchair

Brain-Computer Interface application: auditory serial interface to control a two-class motor-imagery-based wheelchair

Review of real brain-controlled wheelchairs

Decoding the ERD/ERS: influence of afferent input induced by a leg assistive robot

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