Audio-cued motor imagery-based brain–computer interface: Navigation through virtual and real environments

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

doi:10.1016/j.neucom.2012.11.038

Cited by 42 publications

(38 citation statements)

References 27 publications

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“…The calibration phase took approximately 30 minutes, excluding the time needed to set up the EEG recording apparatus. If the participant's EEG data could be classified by a linear discriminant analysis (LDA) classifier with a minimum estimated error below 30%, then the participant proceeded with the adaptation phase, in which he/she was allowed to freely use the navigation application proposed in [8] to navigate through a VE in order to get accustomed to its visual and auditory interface. This part took 5-10 minutes.…”

Section: Methodsmentioning

confidence: 99%

“…For this we followed a paradigm that was used in a previous study [8] to compare user performance when navigating a mobile robot on a real environment with the performance on a virtual environment that closely resembled the former.…”

Section: Introductionmentioning

confidence: 99%

“…For each channel and each trial, we computed the difference between the power spectra of two 1-s intervals: an active interval, during which one of the two mental tasks had taken place, with respect to a 1-s baseline interval between 0.5 and 1.5 seconds after the beginning of the trial [8]. Then, we bandpass filtered that difference signal and used the resultant average power as a feature.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Training in Realistic Virtual Environments: Impact on User Performance in a Motor Imagery-Based Brain–Computer Interface

Silva-Sauer¹,

Aguayo

Velasco-Álvarez³

et al. 2015

Advances in Computational Intelligence

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Training in Realistic Virtual Environments: Impact on User Performance in a Motor Imagery-Based Brain–Computer Interface

Silva-Sauer¹,

Aguayo

Velasco-Álvarez³

et al. 2015

Advances in Computational Intelligence

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“…In [42], the same paradigm used in [35] (first visual and then auditory) provided four commands to move a robot in a small maze of corridors with both discrete and continuous movements.…”

Section: Robotsmentioning

confidence: 99%

BCI-Based Navigation in Virtual and Real Environments

Velasco-Álvarez

Ron-Angevín

López-Gordo

2013

Advances in Computational Intelligence

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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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“…Significant work has been done to match the number of commands to the number of mental tasks [10,11]. For example, by incorporating motor imagery EEG with auditory BCI, F. Velasco-Álvarez operated only two mental tasks: relaxed state versus imagination of right hand movements to reduce the probability of misclassification [12]. Now four generally accepted common classes of ERD/ERS based EEG can be ensured for recognition and classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Nonholonomic mobile system control by combining EEG-based BCI with ANFIS

Wang

Feng

et al. 2015

BME

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Abstract. Motor imagery EEG-based BCI has advantages in the assistance of human control of peripheral devices, such as the mobile robot or wheelchair, because the subject is not exposed to any stimulation and suffers no risk of fatigue. However, the intensive training necessary to recognize the numerous classes of data makes it hard to control these nonholonomic mobile systems accurately and effectively. This paper proposes a new approach which combines motor imagery EEG with the Adaptive Neural Fuzzy Inference System. This approach fuses the intelligence of humans based on motor imagery EEG with the precise capabilities of a mobile system based on ANFIS. This approach realizes a multi-level control, which makes the nonholonomic mobile system highly controllably without stopping or relying on sensor information. Also, because the ANFIS controller can be trained while performing the control task, control accuracy and efficiency is increased for the user. Experimental results of the nonholonomic mobile robot verify the effectiveness of this approach.

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Audio-cued motor imagery-based brain–computer interface: Navigation through virtual and real environments

Cited by 42 publications

References 27 publications

Training in Realistic Virtual Environments: Impact on User Performance in a Motor Imagery-Based Brain–Computer Interface

Training in Realistic Virtual Environments: Impact on User Performance in a Motor Imagery-Based Brain–Computer Interface

BCI-Based Navigation in Virtual and Real Environments

Nonholonomic mobile system control by combining EEG-based BCI with ANFIS

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