Study of the Length of time Window in Emotion Recognition based on EEG Signals.
The objective of this research is to present a comparative analysis using various lengths of time windows (TW) during emotion recognition, employing machine learning techniques and the portable wireless sensing device EPOC+. In this study, entropy will be utilized as a feature to evaluate the perfor...
| Publicado en: | Revista Mexicana de Ingeniería Biomédica Vol. 45; no. 1; pp. 31 - 43 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Sociedad Mexicana de Ingenieria Biomedica, A.C.
Jan-Apr2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=lth&AN=177354696&site=ehost-live header: @attributes: shortDbName: lth uiTerm: 177354696 longDbName: MedicLatina uiTag: AN controlInfo: bkinfo: jinfo: jid: 01889532 7L3 jtl: Revista Mexicana de Ingeniería Biomédica issn: 01889532 maglogo: N pubinfo: dt: Jan-Apr2024 vid: 45 iid: 1 pid: 20850 pub: Sociedad Mexicana de Ingenieria Biomedica, A.C. artinfo: ui: 177354696 10.17488/RMIB.45.1.3 ppf: 31 ppct: 12 formats: fmt: @attributes: type: P size: 2.1MB tig: atl: Study of the Length of time Window in Emotion Recognition based on EEG Signals. aug: au: Jarillo Silva, Alejandro Gómez Pérez, Víctor Alberto Domínguez Ramírez, Omar Arturo affil: Universidad de la Sierra Sur, Oaxaca - México Universidad Autónoma del Estado de Hidalgo, Hidalgo - México su: Emotion recognition Electroencephalography Machine learning Cohen's kappa coefficient (Statistics) K-nearest neighbor classification Support vector machines Logistic regression analysis Random forest algorithms sug: subj: Emotion recognition Electroencephalography Machine learning Cohen's kappa coefficient (Statistics) K-nearest neighbor classification Support vector machines Logistic regression analysis Random forest algorithms keyword: electroencephalogram emotion recognition machine learning time window length aprendizaje automático electroencefalograma longitud de ventana de tiempo reconocimiento de emociones ab: The objective of this research is to present a comparative analysis using various lengths of time windows (TW) during emotion recognition, employing machine learning techniques and the portable wireless sensing device EPOC+. In this study, entropy will be utilized as a feature to evaluate the performance of different classifier models across various TW lengths, based on a dataset of EEG signals extracted from individuals during emotional stimulation. Two types of analyses were conducted: between-subjects and within-subjects. Performance measures such as accuracy, area under the curve, and Cohen's Kappa coefficient were compared among five supervised classifier models: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Decision Trees (DT). The results indicate that, in both analyses, all five models exhibit higher performance in TW ranging from 2 to 15 seconds, with the 10 seconds TW particularly standing out for between-subjects analysis and the 5-second TW for within-subjects; furthermore, TW exceeding 20 seconds are not recommended. These findings provide valuable guidance for selecting TW in EEG signal analysis when studying emotions. El objetivo de esta investigación es presentar un análisis comparativo empleando diversas longitudes de ventanas de tiempo (VT) durante el reconocimiento de emociones, utilizando técnicas de aprendizaje automático y el dispositivo de sensado inalámbrico portátil EPOC+. En este estudio, se utilizará la entropía como característica para evaluar el rendimiento de diferentes modelos clasificadores en diferentes longitudes de VT, basándose en un conjunto de datos de señales EEG extraídas de individuos durante la estimulación de emociones. Se llevaron a cabo dos tipos de análisis: entre sujetos e intra-sujetos. Se compararon las medidas de rendimiento, tales como la exactitud, el área bajo la curva y el coeficiente de Cohen's Kappa, de cinco modelos clasificadores supervisados: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF) y Decision Trees (DT). Los resultados indican que, en ambos análisis, los cinco modelos presentan un mayor rendimiento en VT de 2 a 15 segundos, destacándose especialmente la VT de 10 segundos para el análisis entre los sujetos y 5 segundos intrasujetos; además, no se recomienda utilizar VT superiores a 20 segundos. Estos hallazgos ofrecen una orientación valiosa para la elección de las VT en el análisis de señales EEG al estudiar las emociones. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Revista Mexicana de Ingeniería Biomédica is the property of Sociedad Mexicana de Ingenieria Biomedica, A.C. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Revista Mexicana de Ingeniería Biomédica holder: Sociedad Mexicana de Ingenieria Biomedica, A.C. dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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