Comparison of SVM and ANN for classification of eye events in EEG

Singla, Rajesh; Chambayil, Brijil; Khosla, Arun; Santosh, J.

doi:10.4236/jbise.2011.41008

Cited by 57 publications

(31 citation statements)

References 2 publications

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“…The Brain Computer Interface (BCI) system provides a direct communication channel between human brain and the computer without using brain's normal output pathways of peripheral nerves and muscles [1,2]. By acquiring and translating the brain signals that are modified according to the intentions, a BCI system can provide an alternative, augmentative communication and control options for individuals with severe neuromuscular disorders, such as spinal cord injury, brain stem stroke and Amyotrophic Lateral Sclerosis (ALS).…”

Section: Introductionmentioning

confidence: 99%

Influence of stimuli color on steady-state visual evoked potentials based BCI wheelchair control

Singla¹,

Khosla²,

Jha³

2013

JBiSE

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In recent years, Brain Computer Interface (BCI) systems based on Steady-State Visual Evoked Potential (SSVEP) have received much attention. This study tries to develop a SSVEP based BCI system that can control a wheelchair prototype in five different positions including stop position. In this study four different flickering frequencies in low frequency region were used to elicit the SSVEPs and were displayed on a Liquid Crystal Display (LCD) monitor using Lab-VIEW. Four stimuli colors, green, red, blue and violet were used to investigate the color influence in SSVEPs. The Electroencephalogram (EEG) signals recorded from the occipital region were segmented into 1 second window and features were extracted by using Fast Fourier Transform (FFT). One-Against-All (OAA), a popular strategy for multiclass SVM, is used to classify SSVEP signals. During stimuli color comparison SSVEP with violet color showed higher accuracy than that with green, red and blue stimuli.

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Section: Introductionmentioning

confidence: 99%

Influence of stimuli color on steady-state visual evoked potentials based BCI wheelchair control

Singla¹,

Khosla²,

Jha³

2013

JBiSE

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“…They presented comparison result in related paper [19]. Razzaghi et all (2015), proposed a multilevel algorithmic framework for SVP that includes (a) construction of hierarchy of large-scale data coarse representations, and b) a local processing of updating the hyper plane throughout this hierarchy.…”

Section: A the Classification Studies On Eeg Eye State Medical Datasetmentioning

confidence: 99%

Performance of Classification Techniques on Medical Datasets

Tutuncu¹,

Köklü²

2015

Third International Conference on Advances in Bio-Informatics and Environmental Engineering - ICABEE 2015

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“…In particular, usually SVM predictors are used instead of another methods as neuronal networks (ANN) due to their better performance in this kind of problems as shown in [2].…”

Section: Introductionmentioning

confidence: 99%

ICA and SVM Clustering Applied to Remove Ocular Artifacts from Electroencephalography

Peña-Rodríguez

Sierra

Conde-Cotes

2017

VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Santander, Colombia, October 26th -28th, 2016

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Abstract-Brain behavior study has taken attention by the scientific world in last years. Comprehension of cerebral processes is made by means of data obtained through several techniques such as: electrocorticography, magnetoencephalography, functional and structural imaging, and electroencephalography (EEG). This latter is the most used due to its low cost and minimal risk. However, recording of cerebral information through EEG is affected by different artifactual sources, which influence their posterior data processing. Inside non-cerebral sources, the potentials caused by ocular movements during tracking and fixing tasks have the greatest impact. For this reason, a procedure to identify and subtract the ocular artifacts from EEG signals is needed. In this work, an automatic method to remove artifacts is presented. The algorithm is compounded of four main stages: a) the separation of the EEG and artifacts sources by means of Independent Component Analysis (ICA); b) the characterization of the EEG and artifact components using complexity features; c) the components classification through a Hybrid Support Vector Machine (SVM)-External clustering; and d) the reconstruction of the EEG free of artifacts. The method has an overall accuracy of 85.9%, the elimination of ocular artifacts is 85.68% and the preservation of EEG information is 85.9%. The entire algorithm was written in Python.

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Comparison of SVM and ANN for classification of eye events in EEG

Abstract: ABSTRACT

Cited by 57 publications

References 2 publications

Influence of stimuli color on steady-state visual evoked potentials based BCI wheelchair control

Influence of stimuli color on steady-state visual evoked potentials based BCI wheelchair control

Performance of Classification Techniques on Medical Datasets

ICA and SVM Clustering Applied to Remove Ocular Artifacts from Electroencephalography

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