Real‐Time EEG‐Based Happiness Detection System

Jatupaiboon, Noppadon; Pan-ngum, Setha; Israsena, Pasin

doi:10.1155/2013/618649

Cited by 196 publications

(116 citation statements)

References 42 publications

Supporting

Mentioning

108

Contrasting

Unclassified

Order By: Relevance

“…And supporting arguments have been given by a number of researchers who have successfully applied this system to assess cognitive processes [12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 88%

Auditory evoked potentials in patients with major depressive disorder measured by Emotiv system

Wang

Yang-de

et al. 2015

BME

View full text Add to dashboard Cite

Abstract. In a previous study (unpublished), Emotiv headset was validated for capturing event-related potentials (ERPs) from normal subjects. In the present follow-up study, the signal quality of Emotiv headset was tested by the accuracy rate of discriminating Major Depressive Disorder (MDD) patients from the normal subjects. ERPs of 22 MDD patients and 15 normal subjects were induced by an auditory oddball task and the amplitude of N1, N2 and P3 of ERP components were specifically analyzed. The features of ERPs were statistically investigated. It is found that Emotiv headset is capable of discriminating the abnormal N1, N2 and P3 components in MDD patients. Relief-F algorithm was applied to all features for feature selection. The selected features were then input to a linear discriminant analysis (LDA) classifier with leave-one-out cross-validation to characterize the ERP features of MDD. 127 possible combinations out of the selected 7 ERP features were classified using LDA. The best classification accuracy was achieved to be 89.66%. These results suggest that MDD patients are identifiable from normal subjects by ERPs measured by Emotiv headset.

show abstract

“…And supporting arguments have been given by a number of researchers who have successfully applied this system to assess cognitive processes [12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 88%

Auditory evoked potentials in patients with major depressive disorder measured by Emotiv system

Wang

Yang-de

et al. 2015

BME

View full text Add to dashboard Cite

show abstract

“…However, the ERP technique has been used in other emotion recognition research [6][7][8][9] because the emotional stimuli were instantaneous pictures. On the other hand, to analyze EEG signals within a certain time duration for successful and productive use in an emotion recognition system, a signal processing technique is needed: one widely-adopted method is to calculate the power spectrum of the brain signals [4,[10][11][12][13], and the results were an approximately 60-70% recognition rate, showing much room for improvement.…”

Section: Literature Reviewmentioning

confidence: 99%

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

2017

View full text Add to dashboard Cite

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.

show abstract

“…The level of arousal might be known through various electrode locations. Regarding recognition of the arousal level the T7 and T8 was chosen based on the computed values of the fractal dimension, which proofed better difference in the level arousal compared to rest of channels [52].…”

Section: Emotional Self-assessmentmentioning

confidence: 99%

“…Based on previous research it is crucial to select the best channel in order to obtain more accurate data [64]. [52] grouped the 14 Emotiv channel into 7 pairs based on their location. Brain activity signals are recognized and analysed in terms of emotional mood ( figure 17).…”

Section: Experiments Designmentioning

confidence: 99%

Emotion Interaction with Virtual Reality Using Hybrid Emotion Classification Technique toward Brain Signals

Kolivand¹,

Abuhashish²,

Zraqou³

et al. 2015

IJCSIT

View full text Add to dashboard Cite

show abstract

Real‐Time EEG‐Based Happiness Detection System

Cited by 196 publications

References 42 publications

Auditory evoked potentials in patients with major depressive disorder measured by Emotiv system

Auditory evoked potentials in patients with major depressive disorder measured by Emotiv system

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

Emotion Interaction with Virtual Reality Using Hybrid Emotion Classification Technique toward Brain Signals

Contact Info

Product

Resources

About