Brain-Computer Interface: Comparison of two paradigms to freely navigate in a virtual environment through one mental task

Velasco-Álvarez, Francisco; Ron-Angevín, Ricardo; Silva-Sauer, Leandro da; Sancha-Ros, Salvador

doi:10.1109/ib2com.2010.5723617

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…The classifier output was updated every 200 ms. In order to change from the no-movement to the movement state, and to prevent rapid changes, the subject had to accumulate more than a 'selection time' with the correct movement selected [54,75]. The selection time is the number of successive correct classifier outputs resulting from MI of a movement.…”

Section: Cue-paced Bci Designmentioning

confidence: 99%

“…The first uses a brain switch control where the feedback is provided only once after the classification (switch-control mode) [53]. The second uses a continuous control, where the feedback is provided continuously for as long as the MI is maintained by the user (continuous-control mode) [54]. Each BCI control mode has its specific benefits to the rehabilitation process, depending on the training program of the rehabilitation process, the current phase within the training program of the rehabilitation process, the level of pathology and the progress of the user within his training program of the rehabilitation process.…”

Section: Introductionmentioning

confidence: 99%

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A multi-modal modified feedback self-paced BCI to control the gait of an avatar

2021

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Brain–computer interfaces (BCIs) have been used to control the gait of a virtual self-avatar with a proposed application in the field of gait rehabilitation. Some limitations of existing systems are: (a) some systems use mental imagery (MI) of movements other than gait; (b) most systems allow the user to take single steps or to walk but do not allow both; (c) most function in a single BCI mode (cue-paced or self-paced). Objective. The objective of this study was to develop a high performance multi-modal BCI to control single steps and forward walking of an immersive virtual reality avatar. Approach. This system used MI of these actions, in cue-paced and self-paced modes. Twenty healthy participants participated in this study, which was comprised of four sessions across four different days. They were cued to imagine a single step forward with their right or left foot, or to imagine walking forward. They were instructed to reach a target by using the MI of multiple steps (self-paced switch-control mode) or by maintaining MI of forward walking (continuous-control mode). The movement of the avatar was controlled by two calibrated regularized linear discriminate analysis classifiers that used the µ power spectral density over the foot area of the motor cortex as a feature. The classifiers were retrained after every session. For a subset of the trials, positive modified feedback (MDF) was presented to half of the participants, where the avatar moved correctly regardless of the classification of the participants’ MI. The performance of the BCI was computed on each day, using different control modes. Main results. All participants were able to operate the BCI. Their average offline performance, after retraining the classifiers was 86.0 ± 6.1%, showing that the recalibration of the classifiers enhanced the offline performance of the BCI (p < 0.01). The average online performance was 85.9 ± 8.4% showing that MDF enhanced BCI performance (p = 0.001). The average performance was 83% at self-paced switch control and 92% at continuous control mode. Significance. This study reports on a first BCI to use motor imagery of the lower limbs in order to control the gait of an avatar with different control modes and different control commands (single steps or forward walking). BCI performance is increased in a novel way by combining three different performance enhancement techniques, resulting in a single high performance and multi-modal BCI system. This study also showed that the improvements due to the effects of MDF lasted for more than one session.

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Section: Cue-paced Bci Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multi-modal modified feedback self-paced BCI to control the gait of an avatar

2021

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“…On a later experiment [34], the same navigation paradigm was used to provide continuous movements: after the selection of a command the movement was kept while the MI task was above certain threshold.…”

Section: 2mentioning

confidence: 99%

BCI-Based Navigation in Virtual and Real Environments

Velasco-Álvarez

Ron-Angevín

López-Gordo

2013

Lecture Notes in Computer Science

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Abstract. A Brain-Computer Interface (BCI) is a system that enables people to control an external device with their brain activity, without the need of any muscular activity. Researchers in the BCI field aim to develop applications to improve the quality of life of severely disabled patients, for whom a BCI can be a useful channel for interaction with their environment. Some of these systems are intended to control a mobile device (e. g. a wheelchair). Virtual Reality is a powerful tool that can provide the subjects with an opportunity to train and to test different applications in a safe environment. This technical review will focus on systems aimed at navigation, both in virtual and real environments.

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“…Thanks to the brain switch the user could be able to maintain a state, e.g., the advance command, without using the MI task for a long time. The switch paradigm has been previously used in a MI based virtual wheelchair by Velasco-Álvarez et al ( 2010 ) and Huang et al ( 2012 ). Besides the paper of Velasco-Álvarez et al ( 2010 ) the BCI group of the University of Malaga (UMA-BCI) has applied this paradigm on the management of a real mobile robot using SMR (Ron-Angevin et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Switch and Continuous Navigation Paradigms to Command a Brain-Controlled Wheelchair

et al. 2018

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A brain-computer interface (BCI) is a technology allowing patients with severe motor dysfunctions to use their electroencephalographic signals to create a communication channel to control devices. The objective of this paper is to study the feasibility of continuous and switch control modes for a brain-controlled wheelchair (BCW) using sensorimotor rhythms (SMR) modulated through a right-hand motor imagery task. Previous studies, which used a continuous navigation control with SMR, have reported the difficulty of maintaining the motor imagery task for a long time, especially for the forward command. The switch control has been presented as a proposal that may help to solve this issue since this task is only used temporary for either disabling or enabling the movement. Regarding the methodology, 10 of 15 able-bodied users, who had overcome the criterion of 30% error rate in the calibration phase, controlled the BCW using both paradigms. The navigation tasks consisted of a straight path divided in five sections: in three of them the users had to move forward, and in the other two the users had to maintain their position. To assess user performance in the device management, a usability approach was adopted, measuring the factors of effectiveness, efficiency, and satisfaction. Then, variables related to the time employed and commands selected by the user or parameters related to the confusion matrix were applied. In addition, the scores in NASA-TLX and two ad hoc questionnaires were considered to discuss the user experience controlling the wheelchair. Despite the results showed that the best system for a specific user relies on his/her abilities and preferences, the switch control mode obtained better accuracy (0.59 ± 0.17 for continuous and 0.72 ± 0.05 for switch). Furthermore, the switch paradigm can be recommended for the advance sections as with it users could complete the advance sections in less time (42.2 ± 28.7 s for continuous and 15.47 ± 3.43 s for switch), while the continuous mode seems to be better at keeping the wheelchair stopped (42.45 ± 16.01 s for continuous and 24.35 ± 10.94 s for switch).

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Brain-Computer Interface: Comparison of two paradigms to freely navigate in a virtual environment through one mental task

Cited by 5 publications

References 11 publications

A multi-modal modified feedback self-paced BCI to control the gait of an avatar

A multi-modal modified feedback self-paced BCI to control the gait of an avatar

BCI-Based Navigation in Virtual and Real Environments

Evaluation of Switch and Continuous Navigation Paradigms to Command a Brain-Controlled Wheelchair

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