A 3-class Asynchronous BCI Controlling A Simulated Mobile Robot

Geng, Tao; Dyson, Matthew; Tsui, Chun Sing Louis; Gan, John Q.

doi:10.1109/iembs.2007.4352842

Cited by 23 publications

(10 citation statements)

References 3 publications

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“…Several three-class BCIs based on motor imagery (MI) have been developed [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. In [ 3 ], authors extracted EEG features using fast Fourier transform (FFT).…”

Section: Introductionmentioning

confidence: 99%

Three-Class EEG-Based Motor Imagery Classification Using Phase-Space Reconstruction Technique

et al. 2016

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Over the last few decades, brain signals have been significantly exploited for brain-computer interface (BCI) applications. In this paper, we study the extraction of features using event-related desynchronization/synchronization techniques to improve the classification accuracy for three-class motor imagery (MI) BCI. The classification approach is based on combining the features of the phase and amplitude of the brain signals using fast Fourier transform (FFT) and autoregressive (AR) modeling of the reconstructed phase space as well as the modification of the BCI parameters (trial length, trial frequency band, classification method). We report interesting results compared with those present in the literature by utilizing sequential forward floating selection (SFFS) and a multi-class linear discriminant analysis (LDA), our findings showed superior classification results, a classification accuracy of 86.06% and 93% for two BCI competition datasets, with respect to results from previous studies.

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“…Several three-class BCIs based on motor imagery (MI) have been developed [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. In [ 3 ], authors extracted EEG features using fast Fourier transform (FFT).…”

Section: Introductionmentioning

confidence: 99%

Three-Class EEG-Based Motor Imagery Classification Using Phase-Space Reconstruction Technique

et al. 2016

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“…The work presented in [30] does not use VR techniques, but it shows the blueprint of an apartment. This experiment used three MI tasks: right or left hand MI made an avatar turn both sides, while feet MI made it advance.…”

Section: 2mentioning

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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“…However, most MI-based BCIs can only discriminate a finite number of MI tasks (usually two or three) and decode spatially well separated MI patterns in brain areas as control commands. For example, left/right hand, foot and/or tongue MI tasks are the most commonly adopted among the MI-based BCI systems [6,12]. Due to the fact that MI tasks within the same upper extremity activate and occupy the same brain regions on the motor cortex area [13,14], the EEG signals have non-stationary and overlapped representations, which makes them really challenging to detect the MI tasks from the same limb.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Different Feature Extraction Methods for Motor Imagery EEG Decoding within the Same Upper Extremity

Chu

Zhao

Zou

et al. 2018

2018 Chinese Automation Congress (CAC)

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Compared to other electroencephalogram (EEG) modalities, motor imagery (MI) based brain-computer interfaces (BCIs) can provide more natural and intuitive communication between human intentions and external machines. However, this type of BCI depends heavily on effective signal processing to discriminate EEG patterns corresponding to various MI tasks, especially feature extraction procedures. In this study, a comparison of different feature extraction methods was conducted for EEG classification of imaginary movements within the same upper extremity. Unlike traditional MI tasks (left/right hand), six imaginary movements from the same unilateral upper extremity were proposed and evaluated, including elbow extension/flexion, wrist pronation/supination, and hand open/grasp. To tackle the classification challenge of MI tasks within the same limb, four types of feature extraction methods were implemented and compared in combination with support vector machine (SVM) and linear discriminant analysis (LDA) classifiers, such as wavelet transformation, power spectrum, autoregressive model, common spatial patterns (CSP) and variants of filter-bank CSP (FBCSP), regularized CSP (RCSP). The overall accuracies of the CSP were significant higher than other three types of feature extraction on a dataset collected from 8 individuals, particularly the SVM with FBCSP had the best performance with an average accuracy of 71.78%. These decoding results of MI tasks during single upper extremity are encouraging and promising in the context of more natural MI-BCI for controlling assisted devices, such as a neuroprosthetic or robotic arm for motor disabled individuals with highly impaired upper extremity.

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A 3-class Asynchronous BCI Controlling A Simulated Mobile Robot

Cited by 23 publications

References 3 publications

Three-Class EEG-Based Motor Imagery Classification Using Phase-Space Reconstruction Technique

Three-Class EEG-Based Motor Imagery Classification Using Phase-Space Reconstruction Technique

BCI-Based Navigation in Virtual and Real Environments

A Comparative Study of Different Feature Extraction Methods for Motor Imagery EEG Decoding within the Same Upper Extremity

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