An approach to EEG-based emotion recognition using combined feature extraction method

Zhang, Yong; Ji, Xiaomin; Zhang, Suhua

doi:10.1016/j.neulet.2016.09.037

Cited by 144 publications

(93 citation statements)

References 20 publications

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“…This research utilized two non-linear classifiers, namely the BPNN and the PNN. The result of using these classifier was also comparable to other recent research using other linear and non-linear classifier, such as SVM [50,53] and DLN [37]. However, newer classifiers, such as group sparse canonical correlation analysis (GCCA) [54] and sparse deep belief networks (SDBN) [55], have never been used, giving room to further future works.…”

Section: Discussionsupporting

confidence: 70%

“…The proposed bispectrum-based feature extraction gave a comparable result with some recent research using different feature extraction methods, such as Discrete Wavelet Transform -Relative Wavelet Energy (DWT-RWE) [3], Short Time Fourier Transform (STFT) [50], Power Spectral Density (PSD) [37] and Empirical Mode Decomposition (EMD) with Sample Entropy (SampEn) [53]. Future studies in the EEG-based emotion recognition system should focus on improving the feature calculation from the bispectrum values.…”

Section: Discussionmentioning

confidence: 53%

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Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural Networks

2017

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Abstract:The development of automatic emotion detection systems has recently gained significant attention due to the growing possibility of their implementation in several applications, including affective computing and various fields within biomedical engineering. Use of the electroencephalograph (EEG) signal is preferred over facial expression, as people cannot control the EEG signal generated by their brain; the EEG ensures a stronger reliability in the psychological signal. However, because of its uniqueness between individuals and its vulnerability to noise, use of EEG signals can be rather complicated. In this paper, we propose a methodology to conduct EEG-based emotion recognition by using a filtered bispectrum as the feature extraction subsystem and an artificial neural network (ANN) as the classifier. The bispectrum is theoretically superior to the power spectrum because it can identify phase coupling between the nonlinear process components of the EEG signal. In the feature extraction process, to extract the information contained in the bispectrum matrices, a 3D pyramid filter is used for sampling and quantifying the bispectrum value. Experiment results show that the mean percentage of the bispectrum value from 5 × 5 non-overlapped 3D pyramid filters produces the highest recognition rate. We found that reducing the number of EEG channels down to only eight in the frontal area of the brain does not significantly affect the recognition rate, and the number of data samples used in the training process is then increased to improve the recognition rate of the system. We have also utilized a probabilistic neural network (PNN) as another classifier and compared its recognition rate with that of the back-propagation neural network (BPNN), and the results show that the PNN produces a comparable recognition rate and lower computational costs. Our research shows that the extracted bispectrum values of an EEG signal using 3D filtering as a feature extraction method is suitable for use in an EEG-based emotion recognition system.

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Section: Discussionsupporting

confidence: 70%

Section: Discussionmentioning

confidence: 53%

Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural Networks

2017

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“…These features are theta (4-8 Hz), slow alpha (8-10 Hz), alpha (8-12 Hz), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30+ Hz), spectral power for 32 electrodes, and the difference between the spectral powers of all the symmetrical pairs of electrodes. For feature elimination, Fisher's linear discriminant was used and the Gaussian naive Baye's is used for the classification.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, the total readings are 25376 chunks for the 32 users and 40 videos. b) The effect of augmentation phase: One of the limitations of machine learning methods is the availability of sufficient training data to get high recognition performance [24]. The EEG data has only 40 one-minute videos for each subject.…”

Section: ) Single-label Eeg Based Emotion Recognitionmentioning

confidence: 99%

EEG-Based Emotion Recognition using 3D Convolutional Neural Networks

Salama¹,

El-Khoribi²,

Shoman³

et al. 2018

ijacsa

160

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Abstract-Emotion recognition is a crucial problem in Human-Computer Interaction (HCI).Various techniques were applied to enhance the robustness of the emotion recognition systems using electroencephalogram (EEG) signals especially the problem of spatiotemporal features learning. In this paper, a novel EEG-based emotion recognition approach is proposed. In this approach, the use of the 3-Dimensional Convolutional Neural Networks (3D-CNN) is investigated using a multi-channel EEG data for emotion recognition. A data augmentation phase is developed to enhance the performance of the proposed 3D-CNN approach. And, a 3D data representation is formulated from the multi-channel EEG signals, which is used as data input for the proposed 3D-CNN model. Extensive experimental works are conducted using the DEAP (Dataset of Emotion Analysis using the EEG and Physiological and Video Signals) data. It is found that the proposed method is able to achieve recognition accuracies 87.44% and 88.49% for valence and arousal classes respectively, which is outperforming the state of the art methods.

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“…By using good and strong stimulation, emotion recognition is more likely to be performed with better results and higher accuracy. There are some types of stimulation as follow: pictures, 12-43 video clips, [44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60] music, [61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76] memories, 77 self-induction, 78,80,81 environment elicitation like light, humidity and temperature, 79 games, 82 etc. Some ways of eliciting emotions and some induced emotions are listed in Table 1.…”

Section: Emotion Stimulationmentioning

confidence: 99%