Virtual Navigation System for the Disabled by Motor Imagery

Suh, Dongjun; Cho, Hyun Sang; Goo, Jayoung James; Park, Kyung S.; Hahn, Minsoo

doi:10.1007/1-4020-5261-8_24

Cited by 8 publications

(6 citation statements)

References 19 publications

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“…3. P C3C4 with 120 parameters obtained only from signals from electrodes C 3 and C 4 , as these were reported by other research to be the most significant for motion classification [25,33,41], verifying if limiting the region of interest to two regions on motor cortex decreases accuracy.…”

Section: Data Classification Proceduressupporting

confidence: 75%

“…accuracy of 0.7 obtained in a left/right navigation employing comparison of signals from electrodes C 3 and C 4 [41]; accuracy up to 0.77 in a virtual walking task, achieved by extracting band powers of signals from C 3 , C Z , C 4 , and classifying with LDA [25]; classification of motion imagery with accuracy up to 0.85 on the same set of electrodes, but additionally employing author's algorithm of iterative Relief based on distance from center for feature selection, and applying classification with SVM [38]; and accuracy up to 0.86 in a task of left/right hand movement, employing feature selection with ANN and genetic algorithms [51]. Moreover, the results were achieved without any dedicated statistical methods employment, such as ICA, SSP described in literature, whereas blink and heartbeat artefacts elimination and signal improvements methods were not required.…”

Section: Discussionmentioning

confidence: 99%

“…It was determined that main phenomenon is alpha wave power decrease in a motor cortex in a hemisphere contra-lateral to the movement side [25,26,33], usually registered by C3 and C4 channels [38,41,51]. It is related to phenomena of event-related de-synchronization (ERD) [21,30,53].…”

Section: Eeg Signal Processing Approachesmentioning

confidence: 99%

“…Published practical research is concerning the subject's mental fatigue often leading to low classification accuracy since the person is not able to concentrate on the task [41]. Other aspects are electrodes positioning, skin conductance, hair thickness, which can be dealt with by hardware solutions such as: an electrode type, tight mounting cap, electrolytic gels, etc.…”

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confidence: 99%

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Real and imaginary motion classification based on rough set analysis of EEG signals for multimedia applications

Szczuko

2017

Multimed Tools Appl

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Rough set-based approach to the classification of EEG signals of real and imaginary motion is presented. The pre-processing and signal parametrization procedures are described, the rough set theory is briefly introduced, and several classification scenarios and parameters selection methods are proposed. Classification results are provided and discussed with their potential utilization for multimedia applications controlled by the motion intent. Accuracy metrics of classification for real and imaginary motion obtained with different parameter sets are compared. Results of experiments employing recorded EEG signals are commented and further research directions are proposed.

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Section: Data Classification Proceduressupporting

confidence: 75%

Section: Discussionmentioning

confidence: 99%

Section: Eeg Signal Processing Approachesmentioning

confidence: 99%

mentioning

confidence: 99%

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Real and imaginary motion classification based on rough set analysis of EEG signals for multimedia applications

Szczuko

2017

Multimed Tools Appl

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“…By using more tasks, a more versatile system can be achieved; it is the case of [28], in which the classification of left and right hand MI is used to turn an avatar in a VE, and the real movement of the feet is used to move forward.…”

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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