EEG Emotion Recognition System

Ma, Li; Chai, Quek Hiok; Kaixiang, Teo; Wahab, Abdul; Abut, H.

doi:10.1007/978-0-387-79582-9_10

Cited by 37 publications

(10 citation statements)

References 4 publications

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“…Three emotion states: pleasant, neutral, and unpleasant were distinguished. By using Relevant Vector Machine, differentiation between happy and relaxed, relaxed and sad, happy and sad with a performance rate around 90% was obtained in work [6].…”

Section: B Emotion Recognition Algorithmsmentioning

confidence: 99%

“…However, little has been done to investigate chaos of brain for emotion recognition. Works [1][2][3][4][5][6] were based on linear analysis, however, linear analysis such as Fourier Transform only preserves the power spectrum in the signal, but destroys the spike-wave structure [17].…”

Section: B Emotion Recognition Algorithmsmentioning

confidence: 99%

“…Emotion recognition could be done from the text, speech, facial expression or gesture. Recently, more researches were done on emotion recognition from EEG [1][2][3][4][5][6]. Traditionally, EEG-based technology has been used in medical applications.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time EEG-Based Human Emotion Recognition and Visualization

Liu

Sourina

Nguyen

2010

2010 International Conference on Cyberworlds

232

102

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Abstract-Emotions accompany everyone in the daily life, playing a key role in non-verbal communication, and they are essential to the understanding of human behavior. Emotion recognition could be done from the text, speech, facial expression or gesture. In this paper, we concentrate on recognition of "inner" emotions from electroencephalogram (EEG) signals as humans could control their facial expressions or vocal intonation. The need and importance of the automatic emotion recognition from EEG signals has grown with increasing role of brain computer interface applications and development of new forms of human-centric and humandriven interaction with digital media. We propose fractal dimension based algorithm of quantification of basic emotions and describe its implementation as a feedback in 3D virtual environments. The user emotions are recognized and visualized in real time on his/her avatar adding one more so-called "emotion dimension" to human computer interfaces.

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Section: B Emotion Recognition Algorithmsmentioning

confidence: 99%

Section: B Emotion Recognition Algorithmsmentioning

confidence: 99%

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Real-Time EEG-Based Human Emotion Recognition and Visualization

Liu

Sourina

Nguyen

2010

2010 International Conference on Cyberworlds

232

102

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“…In the area of biomedical signal processing, a lot of methods has been used in order to produce better quality of information through either time domain or frequency domain. Previously, time domain analysis is very common among EEG study where researchers have been using the same six features namely mean, standard deviation of raw signal, mean of absolute value for first difference of raw signal, mean of absolute value for the first difference of normalized signal, mean of absolute value for second difference of raw signal and mean of absolute value for second difference of normalized signal [6]. However, the emotion classification result from Takahashi et al was only 41.7 % accuracy for 5-class problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Effectiveness of Tuned Q-factor Wavelet Transform in Emotion Recognition Among Left-brain Damaged Stroke Patients

Zheng

Khairunizam

Murugappan

et al. 2019

International Journal of Simulation: Systems, Science &Amp; Technology

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Emotion recognition is impaired among stroke patients due to brain injury. It has given negative impact towards stroke patients because of the difficulty in expressing themselves. Hence, the vision is to overcome this problem by creating a platform to predict the emotion of stroke patients for them so that recurrent stroke events can be avoided. EelectroEncephaloGraph (EEG) of 19 Left Brain Damage patients (LBD) are used as database. The objective of this paper is to compare the accuracy between Tuned Q-factor Wavelet Transform (TQWT) and Wavelet Packet Transform (WPT). The collected raw EEG signals are de-noised by using 6 th order Buttterworth filter. Then, filtered signals were fed into time-frequency analysis tools namely TQWT and WPT to transform the time domain signals into time-frequency domain signals. Hurst exponent feature is extracted from the corresponding time-frequency domain signals before enhancement by using principal component analysis (PCA). Lastly, classification is done through K-nearest neighbour (KNN), probabilistic neural network (PNN) and random forest (RF) in order to evaluate the performance of the recognition system. From the result, it is found that the classification accuracy is consistently higher in TQWT method.

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“…An additional 60 Hz notch filter was applied to avoid power line contamination. Ag/AgCl electrodes were placed on the F4 and T4 positions [28] according to the International 10-20 system, as shown in Figure 1, and the bilateral mastoids were used as positions of the reference and ground electrodes. In addition, images of the subjects' expressions were recorded on video files while the brain signals were being sampled.…”

Section: Eeg Data Acquisitionmentioning

confidence: 99%

Personality Trait and Facial Expression Filter-Based Brain-Computer Interface

Chin

Lee

2013

International Journal of Advanced Robotic Systems

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In this paper, we present technical approaches that bridge the gap in the research related to the use of braincomputer interfaces for entertainment and facial expressions. Such facial expressions that reflect an individual's personal traits can be used to better realize artificial facial expressions in a gaming environment based on a brain-computer interface. First, an emotion extraction filter is introduced in order to classify emotions on the basis of the users' brain signals in real time. Next, a personality trait filter is defined to classify extrovert and introvert types, which manifest as five traits: very extrovert, extrovert, medium, introvert and very introvert. In addition, facial expressions derived from expression rates are obtained by an extrovert-introvert fuzzy model through its defuzzification process. Finally, we confirm this validation via an analysis of the variance of the personality trait filter, a k-fold cross validation of the emotion extraction filter, an accuracy analysis, a user study of facial synthesis and a test case game.

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EEG Emotion Recognition System

Cited by 37 publications

References 4 publications

Real-Time EEG-Based Human Emotion Recognition and Visualization

Real-Time EEG-Based Human Emotion Recognition and Visualization

Effectiveness of Tuned Q-factor Wavelet Transform in Emotion Recognition Among Left-brain Damaged Stroke Patients

Personality Trait and Facial Expression Filter-Based Brain-Computer Interface

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