Wavelet-based emotion recognition system using EEG signal

Mohammadi, Zohreh; Frounchi, Javad; Amiri, Mahmood

doi:10.1007/s00521-015-2149-8

Cited by 305 publications

(143 citation statements)

References 23 publications

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“…There are 96 signals in each category. Among these, 30 signals are used for training process and the remaining 66 signals are used for testing process D. Jude Hemanth DOI: 10.33969/AIS.2020.21001 9 Journal of Artificial Intelligence and Systems…”

Section: Resultsmentioning

confidence: 99%

EEG signal based Modified Kohonen Neural Networks for Classification of Human Mental Emotions

Hemanth¹

2020

AIS

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Section: Resultsmentioning

confidence: 99%

EEG signal based Modified Kohonen Neural Networks for Classification of Human Mental Emotions

Hemanth¹

2020

AIS

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“…Due to DWT effective multi-resolution capability in analysis of non-stationary signals, we followed our previous work [9] for the feature extraction, in which, DWT was applied on the windowed EEG signals of the selected channels. The EEG signals are windowed due to increasing possibility of the quick detection of the emotional state.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Then, a neural network was trained based on the principal components of the features to classify four types of emotion (joy, sorrow, relaxation and anger) with 67.7% classification rate. Mohammedi et al [9], extracted spectral features including energy and entropy of wavelet coefficients from 10 EEG channels. The maximum classification accuracy using KNN was 84% for arousal and 86% for valence.…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition with Machine Learning Using EEG Signals

Bazgir

Mohammadi

Habibi

2018

2018 25th National and 3rd International Iranian Conference on Biomedical Engineering (ICBME)

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100

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In this research, an emotion recognition system is developed based on valence/arousal model using electroencephalography (EEG) signals. EEG signals are decomposed into the gamma, beta, alpha and theta frequency bands using discrete wavelet transform (DWT), and spectral features are extracted from each frequency band. Principle component analysis (PCA) is applied to the extracted features by preserving the same dimensionality, as a transform, to make the features mutually uncorrelated. Support vector machine (SVM), K-nearest neighbor (KNN) and artificial neural network (ANN) are used to classify emotional states. The crossvalidated SVM with radial basis function (RBF) kernel using extracted features of 10 EEG channels, performs with 91.3% accuracy for arousal and 91.1% accuracy for valence, both in the beta frequency band. Our approach shows better performance compared to existing algorithms applied to the "DEAP" dataset.

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“…EEG signals were decomposed into five ranges. Electroencephalography recognition requires feature extraction from the acquired signal within the specific frequency ranges of delta, theta, alpha, beta, and gamma [42]. Table 1 shows the bands, which are decomposed into different sub-bands.…”

Section: Wavelet Transformmentioning

confidence: 99%

Computational model for detection of abnormal brain connections in children with autism

Askari¹,

Setarehdan²,

Sheikhani³

et al. 2018

J. Integr. Neurosci.

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In neuropsychological disorders significant abnormalities in brain connectivity are observed in some regions. A novel model demonstrates connectivity between different brain regions in children with autism. Wavelet decomposition is used to extract features such as relative energy and entropy from electroencephalograph signals. These features are used as input to a 3Dcellular neural network model that indicates brain connectivity. Results show significant differences and abnormalities in the left hemisphere, (p < 0.05) at electrodes AF3, F3, P7, T7, and O1 in the alpha band, AF3, F7, T7, and O1 in the beta band, and T7 and P7 in the gamma band for children with autism when compared with non-autistic controls. Abnormalities in the connectivity of frontal and parietal lobes and the relations of neighboring regions for all three bands (particularly the gamma band) were detected for autistic children. Evaluation demonstrated the alpha frequency band had the best level of distinction (96.6%) based on the values obtained from a cellular neural network that employed support vector machine methods.Many investigators have studied the EEGs and analyzed the signals of autistic subjects [14][15][16]. Sheikhani et al.[15] conducted a study of EEG signals based on Lempel-Ziv frequency methods (LZ) and the short-time Fourier transform (STFT). After evaluating results,

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Wavelet-based emotion recognition system using EEG signal

Cited by 305 publications

References 23 publications

EEG signal based Modified Kohonen Neural Networks for Classification of Human Mental Emotions

EEG signal based Modified Kohonen Neural Networks for Classification of Human Mental Emotions

Emotion Recognition with Machine Learning Using EEG Signals

Computational model for detection of abnormal brain connections in children with autism

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