Feature Extraction and Selection for Emotion Recognition from EEG

Jenke, Robert; Peer, Angelika; Buss, Martin

doi:10.1109/taffc.2014.2339834

Cited by 805 publications

(455 citation statements)

References 47 publications

Supporting

Mentioning

405

Contrasting

Unclassified

Order By: Relevance

“…Researchers have proposed many different methods for recognizing emotions through EEG signals [2,5]. The fundamental challenge of using EEG signals to detect human emotion lies in understanding how a particular emotional state is represented in the brain and applying the correct computational model to accurately identify that emotion, both automatically and in real time.…”

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

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

show abstract

“…Researchers have made use of a variety of features. Jenke [7] surveyed feature selection and extraction across a variety of studies and classified these as time-domain, frequency-domain, timefrequency domain and multi-electrode features. Time-domain features include event related potentials, signal statistics, Hjorth features, non-stationary index, fractal dimension and higher-order crossings; frequency-domain features include band power and higher order spectra; time-frequency domain features include the Hilbert-Huang spectrum and discrete wavelet transforms; multi-electrode features include magnitude squared coherence estimate and differential and rational asymmetries.…”

Section: Realted Workmentioning

confidence: 99%

“…A total of 7 features are obtained per channel recording leading to 224 features in total. They are straightforward to calculate according to the formulas given in [7], as shown below:…”

Section: Realted Workmentioning

confidence: 99%

See 1 more Smart Citation

Emotion detection from EEG recordings

Liu

Meng

Nandi

et al. 2016

2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

View full text Add to dashboard Cite

Abstract-Human brain behavior is very complex and it is difficult to interpret. Human emotion might come from brain activities. However, the relationship between human emotion and brain activities is far from clear. In recent years, more and more researchers are trying to discover this relationship by recording brain signals such as electroencephalogram (EEG) signals with the associated emotion information extracted from other modalities such as facial expression. In this paper, machine learning based methods are used to model this relationship in the publicly available dataset DEAP (Database for Emotional Analysis using Physiological Signals). Different features are extracted from raw EEG recordings. Then Maximum Relevance Minimum Redundancy (mRMR) was used for feature selection. These features are fed into machine learning methods to build the prediction models to extract the emotion information from EEG signals. The models are evaluated on this dataset and satisfactory results are achieved.

show abstract

“…Based on the neural correlate studies of emotion using EEG data, various algorithms, e.g. fractal dimension (FD), power spectral density (PSD) and discrete wavelet transform, have been proposed to extract meaningful information from EEG data and construct models to recognize human emotion [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Continuous Music-Emotion Recognition Based on Electroencephalogram

Thammasan

Moriyama

Fukui

et al. 2016

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYResearch on emotion recognition using electroencephalogram (EEG) of subjects listening to music has become more active in the past decade. However, previous works did not consider emotional oscillations within a single musical piece. In this research, we propose a continuous music-emotion recognition approach based on brainwave signals. While considering the subject-dependent and changing-over-time characteristics of emotion, our experiment included self-reporting and continuous emotion annotation in the arousal-valence space. Fractal dimension (FD) and power spectral density (PSD) approaches were adopted to extract informative features from raw EEG signals and then we applied emotion classification algorithms to discriminate binary classes of emotion. According to our experimental results, FD slightly outperformed PSD approach both in arousal and valence classification, and FD was found to have the higher correlation with emotion reports than PSD. In addition, continuous emotion recognition during music listening based on EEG was found to be an effective method for tracking emotional reporting oscillations and provides an opportunity to better understand human emotional processes.

show abstract

Feature Extraction and Selection for Emotion Recognition from EEG

Cited by 805 publications

References 47 publications

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

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

Emotion detection from EEG recordings

Continuous Music-Emotion Recognition Based on Electroencephalogram

Contact Info

Product

Resources

About