Classifying Different Emotional States by Means of EEG-Based Functional Connectivity Patterns

Lee, You Yun; Hsieh, Shulan

doi:10.1371/journal.pone.0095415

Cited by 236 publications

(129 citation statements)

References 58 publications

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“…With respect to this, novel methods to quantify functional connectivity among different brain areas have been proposed with the aim of discovering new insights about the brain's response to different emotional processes [15,16]. Thus, these algorithms have been used to identify different emotions elicited by audiovisual stimuli [16][17][18], as well as to characterize mental disorders, such as major depression [19,20], consciousness problems [21], epilepsy [22], Alzheimer's [23] or schizophrenia [24].…”

Section: Introductionmentioning

confidence: 99%

Application of Entropy-Based Metrics to Identify Emotional Distress from Electroencephalographic Recordings

García-Martínez

Martínez-Rodrigo

Cantabrana

et al. 2016

Entropy

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Recognition of emotions is still an unresolved challenge, which could be helpful to improve current human-machine interfaces. Recently, nonlinear analysis of some physiological signals has shown to play a more relevant role in this context than their traditional linear exploration. Thus, the present work introduces for the first time the application of three recent entropy-based metrics: sample entropy (SE), quadratic SE (QSE) and distribution entropy (DE) to discern between emotional states of calm and negative stress (also called distress). In the last few years, distress has received growing attention because it is a common negative factor in the modern lifestyle of people from developed countries and, moreover, it may lead to serious mental and physical health problems. Precisely, 279 segments of 32-channel electroencephalographic (EEG) recordings from 32 subjects elicited to be calm or negatively stressed have been analyzed. Results provide that QSE is the first single metric presented to date with the ability to identify negative stress. Indeed, this metric has reported a discriminant ability of around 70%, which is only slightly lower than the one obtained by some previous works. Nonetheless, discriminant models from dozens or even hundreds of features have been previously obtained by using advanced classifiers to yield diagnostic accuracies about 80%. Moreover, in agreement with previous neuroanatomy findings, QSE has also revealed notable differences for all the brain regions in the neural activation triggered by the two considered emotions. Consequently, given these results, as well as easy interpretation of QSE, this work opens a new standpoint in the detection of emotional distress, which may gain new insights about the brain's behavior under this negative emotion.

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Section: Introductionmentioning

confidence: 99%

Application of Entropy-Based Metrics to Identify Emotional Distress from Electroencephalographic Recordings

García-Martínez

Martínez-Rodrigo

Cantabrana

et al. 2016

Entropy

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show abstract

“…video, music) have also been reported. For example, there have been studies that examined the relationship between EEG and self-reported emotional states for music content [60], [61] and video content (movie) [62]. Takahashi et al examined the role of observable behaviors (body posture, head rotation), which were recorded using an RGB-D camera, to assess viewers' attitudes to TV programs [63].…”

Section: Emotion and Motivationmentioning

confidence: 99%

A Survey on Modeling of Human States in Communication Behavior

Niida

Tsugawa

Suganuma

et al. 2017

IEICE Trans. Commun.

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SUMMARY The Technical Committee on Communication BehaviorEngineering addresses the research question "How do we construct a communication network system that includes users?". The growth in highly functional networks and terminals has brought about greater diversity in users' lifestyles and freed people from the restrictions of time and place. Under this situation, the similarities of human behavior cause traffic aggregation and generate new problems in terms of the stabilization of network service quality. This paper summarizes previous studies relevant to communication behavior from a multidisciplinary perspective and discusses the research approach adopted by the Technical Committee on Communication Behavior Engineering.

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“…In particular, EEG with its high temporal resolution, can detect the immediate responses to emotional stimuli [5] and hence various EEG features are implicated in emotion processes. These features at a single electrode level are: 1) components of event related potentials (ERPs) [6], 2) spectral power in different frequency bands and 3) from multichannel perspective -phase synchronization and coherence [7].…”

Section: Introductionmentioning

confidence: 99%

“…Majority of these EEG features are based on wavelet [11] and Hilbert [12] transform. In [5] measures (correlation, coherence, and phase synchronization) have been used to classify positive, neutral and negative emotions elicited by video-clip, which to the best of our knowledge appears to be the most relevant work for emotion recognition.…”

Section: Introductionmentioning

confidence: 99%

Classifying human emotional states using wireless EEG based ERP and functional connectivity measures

Bono

Biswas

Das

et al. 2016

2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)

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Abstract-In this paper we present a systematic exploration to determine several EEG based features for classifying three emotional states (happy, fearful and neutral) pertaining to face perception. EEG data were acquired through a 19-channel wireless system from eight adults under two conditions -in a constrained position and involving head-body movements. The movement EEG data was pre-processed using an artifact reduction algorithm and both datasets were processed to extract neurophysiological features -ERP components and from functional connectivity measures. The functional connectivity measures were processed using a brain connectivity toolbox and gray level co-occurrence matrices to generate a total of 463 features. The feature set was split into: training dataset comprising of constrained and movement EEG data and test dataset comprising of only movement EEG data. A retrospective cross-validation approach was run on the training dataset in conjunction with two classifiers (LDA and SVM) and the ranked feature set, to select the best features using a sequential forward selection algorithm. The best features were further used to prospectively classify the three emotions in the test dataset. Our results show that we can successfully classify the emotions using LDA with an accuracy of 89% and using top 17 ranked features.

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Classifying Different Emotional States by Means of EEG-Based Functional Connectivity Patterns

Cited by 236 publications

References 58 publications

Application of Entropy-Based Metrics to Identify Emotional Distress from Electroencephalographic Recordings

Application of Entropy-Based Metrics to Identify Emotional Distress from Electroencephalographic Recordings

A Survey on Modeling of Human States in Communication Behavior

Classifying human emotional states using wireless EEG based ERP and functional connectivity measures

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