Exploring dimensionality reduction of EEG features in motor imagery task classification

García-Laencina, Pedro J.; Rodríguez-Bermúdez, Germán; Roca-Dorda, Joaquín

doi:10.1016/j.eswa.2014.02.043

Cited by 65 publications

(45 citation statements)

References 44 publications

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“…Usually, the BCI system can be utilized to restore the motor functions or to offer mobility for the motor disabled individuals by using a BCI controlled device, such as the motorized wheelchairs or service robots (Rebsamen et al, 2006;Ron-Angevin, Velasco-Alvarez, Sancha-Ros, & da Silva-Sauer, 2011;Velasco-Álvarez, RonAngevin, da Silva-Sauer, & Sancha-Ros, 2013). MI task is one of the most studied types of EEG signals in BCI systems (García-Laencina, Rodríguez-Bermudez, & Roca-Dorda, 2014). Most of BCI systems based on MI tasks allow user to control the devices in the virtual or physical environment (Barbosa, Achanccaray, & Meggiolaro, 2010;Millan, Renkens, Mouriño, & Gerstner, 2004;Tsui, Gan, & Roberts, 2009).…”

Section: Introductionmentioning

confidence: 99%

Brain Computer Interface system based on indoor semi-autonomous navigation and motor imagery for Unmanned Aerial Vehicle control

Shi

Wang

Zhang

2015

Expert Systems with Applications

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Section: Introductionmentioning

confidence: 99%

Brain Computer Interface system based on indoor semi-autonomous navigation and motor imagery for Unmanned Aerial Vehicle control

Shi

Wang

Zhang

2015

Expert Systems with Applications

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“…In BCI, researchers have applied different techniques in step 3 [4,10,11]. Power spectrum density (PSD) has been computed as a feature extraction approach to test the ELM as a suitable method to be implemented in BCI systems to classify EEG signals from the first session of novice users.…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

“…In a MI BCI experiment an accuracy between 80% and 90% is expected after 6-9 training sessions of 20 minutes [18]. Nevertheless, and according to the stateof-the-art, certain subjects may face difficulties to use MIbased BCI systems and, in these cases, the classification performances are quite poor even using multiple training sessions [11]. Therefore, it is expected a previous selection of subjects with good classification performances in the experiments.…”

Section: Introductionmentioning

confidence: 99%

Classifying BCI signals from novice users with extreme learning machine

2017

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Brain computer interface (BCI) allows to control external devices only with the electrical activity of the brain. In order to improve the system, several approaches have been proposed. However it is usual to test algorithms with standard BCI signals from experts users or from repositories available on Internet. In this work, extreme learning machine (ELM) has been tested with signals from 5 novel users to compare with standard classification algorithms. Experimental results show that ELM is a suitable method to classify electroencephalogram signals from novice users.

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“…Various feature extraction techniques have been used in the BCI literature such as common spatial pattern (CSP) [7,8], Fourier transform [9], wavelet transform [10], power spectral density analysis [11], filtering methods [12,13], polynomial coefficients [6], and autoregressive model [14,15]. Amid these techniques, CSP is one of the very widely used feature extraction techniques in motor imagery-based BCI applications.…”

Section: Introductionmentioning

confidence: 99%

Common spatial pattern-based feature extraction from the best time segment of BCI data

Aydemir

2016

Turk J Elec Eng & Comp Sci

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Abstract:Feature extraction is one of the most crucial stages in the field of brain computer interface (BCI). Because of its ability to directly influence the performance of BCI systems, recent studies have generally investigated how to modify existing methods or develop novel techniques. One of the most successful and well-known methods in BCI applications is the common spatial pattern (CSP). In existing CSP-based methods, the spatial filters were extracted either by using the whole data trial or by dividing the trials into a number of overlapping/nonoverlapping time segments. In this paper, we developed a CSP-based moving window technique to obtain the most distinguishable CSP features and increase the classifier performance by finding the best time segment of electroencephalogram trials. The extracted features were tested by using support vector machines (SVMs). The performance of the classifier was measured in terms of classification accuracy and kappa coefficient ( κ) . The proposed method was successfully applied to the two-dimensional cursor movement imagery data sets, which were acquired from three healthy human subjects in two sessions on different days. The experiments proved that instead of using the whole data length of EEG trials, extracting CSP features from the best time segment provides higher classification accuracy and κ rates.

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Exploring dimensionality reduction of EEG features in motor imagery task classification

Cited by 65 publications

References 44 publications

Brain Computer Interface system based on indoor semi-autonomous navigation and motor imagery for Unmanned Aerial Vehicle control

Brain Computer Interface system based on indoor semi-autonomous navigation and motor imagery for Unmanned Aerial Vehicle control

Classifying BCI signals from novice users with extreme learning machine

Common spatial pattern-based feature extraction from the best time segment of BCI data

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