Application of SVM-RFE on EEG signals for detecting the most relevant scalp regions linked to affective valence processing

Hidalgo‐Muñoz, Antonio R.; López, Manuel López; Santos, Isabel M.; Pereira, A.T.; Vázquez‐Marrufo, Manuel; Galvao-Carmona, Alejandro; Tomé, Ana Maria

doi:10.1016/j.eswa.2012.10.013

Cited by 48 publications

(27 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…SVM-RFE is a heuristic-based encapsulation feature selection method based on SVM, which is used to study the gene selection of cancer classification. The SVM-RFE is popular in the field of gene analysis and then gradually applied to neuroscience and medical images and achieved good application results [30]. …”

Section: Methodsmentioning

confidence: 99%

“…In the classification based on neuroimaging, several feature selection techniques have been proposed, for example, univariate methods (e.g., t -test) [25], multivariate approaches (e.g., sparse logistic regression) [26], perturbation method [27], and support vector machine recursive feature elimination (SVM-RFE) [28]. SVM-RFE has been successfully implemented in various neuroscience applications [29, 30], but it does not have a good performance on image analysis [31]. In this paper, we improve the process of feature selection by combining the SVM-RFE and covariance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Brain MR Image Classification for Alzheimer’s Disease Diagnosis Based on Multifeature Fusion

Xiao

Ding

Lan

et al. 2017

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

We propose a novel classification framework to precisely identify individuals with Alzheimer's disease (AD) or mild cognitive impairment (MCI) from normal controls (NC). The proposed method combines three different features from structural MR images: gray-matter volume, gray-level cooccurrence matrix, and Gabor feature. These features can obtain both the 2D and 3D information of brains, and the experimental results show that a better performance can be achieved through the multifeature fusion. We also analyze the multifeatures combination correlation technologies and improve the SVM-RFE algorithm through the covariance method. The results of comparison experiments on public Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrate the effectiveness of the proposed method. Besides, it also indicates that multifeatures combination is better than the single-feature method. The proposed features selection algorithm could effectively extract the optimal features subset in order to improve the classification performance.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Brain MR Image Classification for Alzheimer’s Disease Diagnosis Based on Multifeature Fusion

Xiao

Ding

Lan

et al. 2017

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…In this work, the recursive feature elimination (RFE) algorithm, proposed by Guyon et al (2002), and based on the support vector machine (SVM; Ben-Hur et al, 2008), is used. Support vector machine recursive feature elimination (SVM-RFE) has been successfully implemented in various neuroscience applications (De Martino et al, 2008; Chu et al, 2012; Hidalgo-Muñoz et al, 2013a); nevertheless, it has hardly been used for image analyses.…”

Section: Introductionmentioning

confidence: 99%

Regions of interest computed by SVM wrapped method for Alzheimer’s disease examination from segmented MRI

Hidalgo‐Muñoz

Ramı́rez

Górriz

et al. 2014

Front. Aging Neurosci.

View full text Add to dashboard Cite

Accurate identification of the most relevant brain regions linked to Alzheimer’s disease (AD) is crucial in order to improve diagnosis techniques and to better understand this neurodegenerative process. For this purpose, statistical classification is suitable. In this work, a novel method based on support vector machine recursive feature elimination (SVM-RFE) is proposed to be applied on segmented brain MRI for detecting the most discriminant AD regions of interest (ROIs). The analyses are performed both on gray and white matter tissues, achieving up to 100% accuracy after classification and outperforming the results obtained by the standard t-test feature selection. The present method, applied on different subject sets, permits automatically determining high-resolution areas surrounding the hippocampal area without needing to divide the brain images according to any common template.

show abstract

“…These proposed systems aim to explore or improve EEG-based emotion recognition systems. [2,39,41,42,49,50,57,61,63,92,104,108,109,117,131,136,149,152,157,173,174,185,186,189,191,[195][196][197][198][199][200][201][202][203][204][205][206][207][208][209]217,219,[223][224][225]229,[262][263][264][265][266]<...>…”

Section: Monitoringmentioning

confidence: 99%

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

View full text Add to dashboard Cite

Abstract:Recent developments and studies in brain-computer interface (BCI) technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants' emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG)-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme consisting of two categories: research orientation and domains/applications. Our results show considerable growth of EEG-based emotion detection journal publications. This growth reflects an increased research interest in EEG-based emotion detection as a salient and legitimate research area. Such factors as the proliferation of wireless EEG devices, advances in computational intelligence techniques, and machine learning spurred this growth.

show abstract

Application of SVM-RFE on EEG signals for detecting the most relevant scalp regions linked to affective valence processing

Cited by 48 publications

References 27 publications

Brain MR Image Classification for Alzheimer’s Disease Diagnosis Based on Multifeature Fusion

Brain MR Image Classification for Alzheimer’s Disease Diagnosis Based on Multifeature Fusion

Regions of interest computed by SVM wrapped method for Alzheimer’s disease examination from segmented MRI

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Contact Info

Product

Resources

About