EEG Correlates of Different Emotional States Elicited during Watching Music Videos

Kroupi, Eleni; Yazdani, Amirmehdi; Ebrahimi, Touradj

doi:10.1007/978-3-642-24571-8_58

Cited by 69 publications

(38 citation statements)

References 10 publications

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“…Each subject has 40 trials where each trial includes the EEG signals of 32 channels, each signal lasting for 60 s. In the leftmost image of Figure 2, we schematically show the raw EEG signal of the first 10 channels. After that, the power spectrum density (PSD, [14,31,33,39,40]) is extracted as a EEG frequency domain feature from the raw signals. The PSD is estimated with Welch's method in MATLAB (R2016a) using a Hamming window and different time window sizes (1,2,3,4,5,6,8,10,12,15,20,30 and 60 s) with no overlap as parameters.…”

Section: The Construction Of Eeg Mfi Sequencesmentioning

confidence: 99%

Human Emotion Recognition with Electroencephalographic Multidimensional Features by Hybrid Deep Neural Networks

et al. 2017

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Featured Application: The method presented in this study can be applied in many fields, such as mental health care, entertainment consumption behavior, society safety, and so on. For example, in the mental health care field, an automatic emotion analysis system can be constructed with our method to monitor the emotional variation of the subjects. With accurate and objective emotion analysis results from EEG signals, our method can provide useful treatment effect information to the medical staff.Abstract: The aim of this study is to recognize human emotions by electroencephalographic (EEG) signals. The innovation of our research methods involves two aspects: First, we integrate the spatial characteristics, frequency domain, and temporal characteristics of the EEG signals, and map them to a two-dimensional image. With these images, we build a series of EEG Multidimensional Feature Image (EEG MFI) sequences to represent the emotion variation with EEG signals. Second, we construct a hybrid deep neural network to deal with the EEG MFI sequences to recognize human emotional states where the hybrid deep neural network combined the Convolution Neural Networks (CNN) and Long Short-Term-Memory (LSTM) Recurrent Neural Networks (RNN). Empirical research is carried out with the open-source dataset DEAP (a Dataset for Emotion Analysis using EEG, Physiological, and video signals) using our method, and the results demonstrate the significant improvements over current state-of-the-art approaches in this field. The average emotion classification accuracy of each subject with CLRNN (the hybrid neural networks that we proposed in this study) is 75.21%.

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Section: The Construction Of Eeg Mfi Sequencesmentioning

confidence: 99%

Human Emotion Recognition with Electroencephalographic Multidimensional Features by Hybrid Deep Neural Networks

et al. 2017

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“…Nonlinear features, including fractal dimension (FD) [3, 4], sample entropy [5], and nonstationary index [6], are utilized for emotion recognition. Hjorth features [7] had also been used in EEG studies [8, 9].…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition from EEG Signals Using Multidimensional Information in EMD Domain

Zhuang

Zeng

et al. 2017

BioMed Research International

212

124

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This paper introduces a method for feature extraction and emotion recognition based on empirical mode decomposition (EMD). By using EMD, EEG signals are decomposed into Intrinsic Mode Functions (IMFs) automatically. Multidimensional information of IMF is utilized as features, the first difference of time series, the first difference of phase, and the normalized energy. The performance of the proposed method is verified on a publicly available emotional database. The results show that the three features are effective for emotion recognition. The role of each IMF is inquired and we find that high frequency component IMF1 has significant effect on different emotional states detection. The informative electrodes based on EMD strategy are analyzed. In addition, the classification accuracy of the proposed method is compared with several classical techniques, including fractal dimension (FD), sample entropy, differential entropy, and discrete wavelet transform (DWT). Experiment results on DEAP datasets demonstrate that our method can improve emotion recognition performance.

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“…Work by Ansari-Asl et al reports results of the application of synchronization likelihood for electrode selection which was tested on one subject only [9]. Recently, Kroupi et al did a study on EEG correlates of emotional states labeled in continuous space [18]. Different features were correlated with a self-assessed emotional measure.…”

Section: Relation To Prior Workmentioning

confidence: 99%

Effect-size-based electrode and feature selection for emotion recognition from EEG

Jenke¹,

Peer²,

Buss³

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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Emotion recognition from EEG signals allows the direct assessment of the "inner" state of the user which is considered an important factor in Human-Machine-Interaction. Given the vast amount of possible features from scalp recordings and the high variance between subjects, a major challenge is to select electrodes and features that separate classes well. In most cases, this decision is made based on neuroscientific knowledge. We propose a statistically-motivated electrode/feature selection procedure, based on Cohen's effect size f 2 . We compare inter-and intra-individual selection on a self-recorded database. Classification is evaluated using quadratic discriminant analysis (QDA). We found both feature selection versions based on f 2 yield comparable results. While highest accuracies up to 57,5% (5 classes) are reached by applying intra-individual selection, inter-individual analysis successfully finds features that perform with lower variance in recognition rates across subjects than combinations of electrodes/features suggested in literature.

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EEG Correlates of Different Emotional States Elicited during Watching Music Videos

Cited by 69 publications

References 10 publications

Human Emotion Recognition with Electroencephalographic Multidimensional Features by Hybrid Deep Neural Networks

Human Emotion Recognition with Electroencephalographic Multidimensional Features by Hybrid Deep Neural Networks

Emotion Recognition from EEG Signals Using Multidimensional Information in EMD Domain

Effect-size-based electrode and feature selection for emotion recognition from EEG

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