Filters








969 Hits in 5.7 sec

Emotion classification using flexible analytic wavelet transform for electroencephalogram signals

Varun Bajaj, Sachin Taran, Abdulkadir Sengur
2018 Health Information Science and Systems  
FAWT analyzes the EEG signal into subbands and statistical measures are computed from the sub-bands for extraction of emotion specific information.  ...  In this paper, electroencephalogram (EEG) database of four emotions (happy, fear, sad, and relax) is recorded and flexible analytic wavelet transform (FAWT) is proposed for the emotion classification.  ...  Acknowledgement Support obtained from the PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur, project titled Brain computer interface for classification of human Emotion,  ... 
doi:10.1007/s13755-018-0048-y pmid:30279982 pmcid:PMC6143498 fatcat:plqkliaicjf37likuug2afh2vu

A Review of the Role of Machine Learning Techniques towards Brain–Computer Interface Applications

Saim Rasheed
2021 Machine Learning and Knowledge Extraction  
It also reviews the ML methods used for mental state detection, mental task categorization, emotion classification, electroencephalogram (EEG) signal classification, event-related potential (ERP) signal  ...  This review article provides a deep insight into the Brain–Computer Interface (BCI) and the application of Machine Learning (ML) technology in BCIs.  ...  (LDA), 68.56% (SVM) 66.42% [57] 2016 FFT MLP-ANN, KNN, SVM, logistic regression Classification accuracy (MLP-ANN), 56.71% (KNN), 68.97% (SVM), 73.03% (logistic EEG signal categorization Classified EEG  ... 
doi:10.3390/make3040042 fatcat:nlhi7qtlcffphpoyvytvql2bmy

A Comparative Analysis of Time-frequency Feature Extraction Techniques for Large Scale Electroencephalogram Data

2021 International Journal of Advanced Trends in Computer Science and Engineering  
Electroencephalogram (EEG) is another approach of recognizing human emotion through brain signals and has offered promising findings.  ...  Although EEG signals provide detail information on human emotional states, the analysis of non-linear and chaotic characteristics of EEG signals is a substantial problem.  ...  ACKNOWLEDGEMENT We thank the anonymous referees for their useful suggestions.  ... 
doi:10.30534/ijatcse/2021/031012021 fatcat:xeoca7cfbrb7tpvy3nd7t3l7ci

Emotion Recognition with Machine Learning Using EEG Signals [article]

Omid Bazgir, Zeynab Mohammadi, Seyed Amir Hassan Habibi
2019 arXiv   pre-print
Support vector machine (SVM), K-nearest neighbor (KNN) and artificial neural network (ANN) are used to classify emotional states.  ...  In this research, an emotion recognition system is developed based on valence/arousal model using electroencephalography (EEG) signals.  ...  Mahmood Amiri of the Kermanshah University of Medical Sciences, and Javad Frounchi from the department of Electrical and Computer Engineering of University of Tabriz for their support and guidance.  ... 
arXiv:1903.07272v1 fatcat:cqefvhymdjh57oxzcwr4dvhrpy

Applying machine learning EEG signal classification to emotion‑related brain anticipatory activity

Marco Bilucaglia, Gian Marco Duma, Giovanni Mento, Luca Semenzato, Patrizio E. Tressoldi
2021 F1000Research  
Classification performance of three different classifiers (LDA, SVM and kNN) was compared using both spectral and temporal features.  ...  For this reason, we analysed previously recorded EEG activity measured while emotional stimuli, high and low arousal (auditory and visual) were provided to a group of healthy participants.  ...  learning approach with EEG signals and also for the investigation of preparatory brain activity.  ... 
doi:10.12688/f1000research.22202.3 fatcat:wzjwuoa2bnayhkslmws4xmt7t4

Applying machine learning EEG signal classification to emotion‑related brain anticipatory activity

Marco Bilucaglia, Gian Marco Duma, Giovanni Mento, Luca Semenzato, Patrizio E. Tressoldi
2021 F1000Research  
Classification performance of three different classifiers (LDA, SVM and kNN) was compared using both spectral and temporal features.  ...  For this reason, we analysed previously recorded EEG activity measured while emotional stimuli, high and low arousal (auditory and visual) were provided to a group of healthy participants.  ...  learning approach with EEG signals and also for the investigation of preparatory brain activity.  ... 
doi:10.12688/f1000research.22202.2 fatcat:jnxxvmtlwvh5ja4fxf3fsrmhx4

Multi-Classifier Fusion Based on MI–SFFS for Cross-Subject Emotion Recognition

Haihui Yang, Shiguo Huang, Shengwei Guo, Guobing Sun
2022 Entropy  
Finally, cross-subject classification accuracies of 0.7089, 0.7106 and 0.7361 are achieved by the SVM, KNN and RF classifiers, respectively.  ...  With the widespread use of emotion recognition, cross-subject emotion recognition based on EEG signals has become a hot topic in affective computing.  ...  In the test process, SVM, KNN and RF were used as classifiers to calculate the accuracy of emotional classification. The average emotion recognition accuracies reached 0.6789, 0.688 and 0.7133.  ... 
doi:10.3390/e24050705 pmid:35626587 pmcid:PMC9141183 fatcat:awaxlthdardn7d2gfh3gczo4ye

EEG based Emotion Recognition and Classification: a Review

Ramprasad Kumawat, Manish Jain
2021 International Research Journal on Advanced Science Hub  
This paper provides an overview of comparative study of various techniques of emotion recognition from EEG signals.  ...  Our analysis is based on extracted features and classification methods of emotion recognition.  ...  In [13] tunable-Q wavelet transform (TQWT) is used for the classification of various emotions of EEG signals. tunable-Q wavelet transform divides EEG signal into subbands and then time-domain features  ... 
doi:10.47392/irjash.2021.131 fatcat:gzujf5t33ndadj6qkgzraqeb74

Applying machine learning EEG signal classification to emotion‑related brain anticipatory activity

Marco Bilucaglia, Gian Marco Duma, Giovanni Mento, Luca Semenzato, Patrizio Tressoldi
2020 F1000Research  
Classification performance of three different classifiers (linear discriminant analysis, support vector machine and k-nearest neighbour) was compared using both spectral and temporal features.  ...  For this reason, we analysed previously recorded EEG activity measured while emotional stimuli, high and low arousal (auditory and visual) were provided to a group of healthy participants.  ...  learning approach with EEG signals and also for the investigation of preparatory brain activity.  ... 
doi:10.12688/f1000research.22202.1 fatcat:q67wi37wwfdqdm35fiy2mnttie

Complexity and Entropy Analysis to Improve Gender Identification from Emotional-Based EEGs

Noor Kamal Al-Qazzaz, Mohannad K. Sabir, Sawal Hamid Bin Mohd Ali, Siti Anom Ahmad, Karl Grammer, Cosimo Ieracitano
2021 Journal of Healthcare Engineering  
from the EEG dataset. k -nearest neighbors kNN and support vector machine (SVM) classification techniques were considered for automatic gender recognition from emotional-based EEGs.  ...  The main novelty of this paper is twofold: first, to investigate Hur as a complexity feature and AAPE as an irregularity parameter for the emotional-based EEGs using two-way analysis of variance (ANOVA  ...  Acknowledgments e authors would like to express their gratitude to the 10 volunteers who participated in this experiment. is work was supported by Universiti Kebangsaan Malaysia and Ministry of Education  ... 
doi:10.1155/2021/8537000 pmid:34603651 pmcid:PMC8481061 fatcat:dszxwvx6tfhbxhdp67ed2uzy4q

Comparative Analysis of Electroencephalogram-Based Classification of User Responses to Statically vs. Dynamically Presented Visual Stimuli

Lin Chew, Jason Teo, James Mountstephens
2015 British Journal of Mathematics & Computer Science  
The exploration of using EEG in understanding  ...  Emotion is an important part of human and it plays important role in human communication.  ...  The average accuracy of 3D motion shapes are better than the average accuracy of the 2D static emotional images for both SVM and KNN with 69.88% and 56.35% using SVM for 3D motion shapes and emotional  ... 
doi:10.9734/bjmcs/2015/19540 fatcat:su3eyonspvfidcpm2uyy7pwho4

EEG-Based Emotion Recognition Using Frequency Domain Features and Support Vector Machines [chapter]

Xiao-Wei Wang, Dan Nie, Bao-Liang Lu
2011 Lecture Notes in Computer Science  
To evaluate classification performance, knearest neighbor (kNN) algorithm, multilayer perceptron and support vector machines are used as classifiers.  ...  After pre-processing the EEG signals, we investigate various kinds of EEG features to build an emotion recognition system.  ...  This research was supported in part by the National Natural Science Foundation of China (Grant No. 90820018), the National Basic  ... 
doi:10.1007/978-3-642-24955-6_87 fatcat:oy4jnhfj3jdrzdka5fsxkn6yla

Predict Students' Attention in Online Learning Using EEG Data

Abeer Al-Nafjan, Mashael Aldayel
2022 Sustainability  
Then, we built three different classification algorithms: k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF).  ...  electroencephalography (EEG) signals for the detection of student's attention during online classes.  ...  This data can be found here: https://www.kaggle.com/code/mashaelaldayel/feature-extraction-and-comparisonof-eeg-bcn (accessed on 20 March 2022).  ... 
doi:10.3390/su14116553 fatcat:6frfrwehkvfu3ov4yjjnu2h7ay

Emotion recognition from multichannel EEG signals using K-nearest neighbor classification

Mi Li, Hongpei Xu, Xingwang Liu, Shengfu Lu, Carlos Gómez, Severin P. Schwarzacher, Huiyu Zhou
2018 Technology and Health Care  
OBJECTIVE: This paper explores the influence of the emotion recognition accuracy of EEG signals in different frequency bands and different number of channels.  ...  Next, EEG signals were divided into four frequency bands using discrete wavelet transform, and entropy and energy were calculated as features of K-nearest neighbor Classifier.  ...  Conflict of interest None to report. M. Li et al. / Emotion recognition from multichannel EEG signals using KNN classification S519  ... 
doi:10.3233/thc-174836 pmid:29758974 pmcid:PMC6027901 fatcat:p6ccsh3phjb2hccxzd2mb2aeii

Emotion classification based on brain wave: a survey

Ting-Mei Li, Han-Chieh Chao, Jianming Zhang
2019 Human-Centric Computing and Information Sciences  
Many researchers used different classification methods and proposed methods for the classification of brain wave emotions.  ...  In this paper, we investigate the existing methods of brain wave emotion classification and describe various classification methods.  ...  This work was supported in part by the Ministry of Science and Technology of Taiwan, R.O.C., under Contracts MOST 105-2221-E-197-010-MY2 and 106-2511-S-259 -001-MY3.  ... 
doi:10.1186/s13673-019-0201-x fatcat:5uaxictpnvdu7i3bhzvzrpms6m
« Previous Showing results 1 — 15 out of 969 results