Automated estimation of human emotion from EEG using statistical features and SVM

Velchev, Yuliyan; Radeva, Svetla; Sokolov, Strahil; Radev, Dimitar

doi:10.1109/dmiaf.2016.7574899

Cited by 16 publications

(7 citation statements)

References 7 publications

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“…Similar approaches have been reported in [3], [5], [17], [25], [26], [37]- [39], to give examples but a few. These approaches utilized typical classification frameworks in which the recorded EEG signals are pre-processed by using spatiotemporal filtering and noise reduction methods, to abate artefacts and enhance the Signal-to-Noise Power ratio (SNR).…”

Section: Eeg Data a Motivationsupporting

confidence: 56%

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Class discovery from semi-structured EEG data for affective computing and personalisation

Ayesh

Arevalillo‐Herráez

Arnau-González

2017

2017 IEEE 16th International Conference on Cognitive Informatics &Amp; Cognitive Computing (ICCI*CC)

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Many approaches to recognising emotions from metrical data such as EEG signals rely on identifying a very small number of classes and to train a classifier. The interpretation of these classes varies from a single emotion such as stress [24] to features of emotional model such as valence-arousal [4]. There are two major issues here. First classification approach limits the analysis of the data within the selected classes and is also highly dependent on training data/cycles, all of which limits generalisation. Second issue is that it does not explore the interrelationships between the data collected missing out on any correlations that could tell us interesting facts beyond emotional recognition. This second issue would be of particular interest to psychologists and medical professions. In this paper, we investigate the use of Self-Organizing Maps (SOM) in identifying clusters from EEG signals that could then be translated into classes. We start by training varying sizes of SOM with the EEG data provided in a public dataset (DEAP). The produced graphs showing Neighbour Distance, Sample Hits, Weight Position are analysed holistically to identify patterns in the structure. Following that, we have considered the groundtruth label provided in DEAP, in order to identify correlations between the label and the clustering produced by the SOM. The results show the potential of SOM for class discovery in this particular context. We conclude with a discussion on the implications of this work and the difficulties in evaluating the outcome.

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Section: Eeg Data a Motivationsupporting

confidence: 56%

“…It stands out as one of the most frequently used to evaluate the performance of emotion recognition methods e.g. [5], [17], [25], [26], [37], [38]. Due to its wide use, it has become a common benchmark in this particular context.…”

Section: Eeg Data a Motivationmentioning

confidence: 99%

Class discovery from semi-structured EEG data for affective computing and personalisation

Ayesh

Arevalillo‐Herráez

Arnau-González

2017

2017 IEEE 16th International Conference on Cognitive Informatics &Amp; Cognitive Computing (ICCI*CC)

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“…The PCA method can effectively reduce the dimension and lower the time complexity at the same time. After PCA processing, the reduced data are regarded as the feature vector, and the SVM method [28][29][30][31] is used for feature classification.…”

Section: Svm Joint Methodsmentioning

confidence: 99%

Novel joint algorithm based on EEG in complex scenarios

Chen

Yang

Miao

et al. 2019

Computer Assisted Surgery

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At present, in the field of electroencephalogram (EEG) signal recognition, the classification and recognition in complex scenarios with more categories of EEG signals have gained more attention. Based on the joint fast Fourier transform (FFT) and support vector machine (SVM) methods, this study proposed a novel EEG signal-processing joint method for the complex scenarios with 10 classifications of EEG signals. Moreover, a comprehensive efficiency formula was put forward. The formula considered the accuracy and time consumption of the joint method. This new joint method could improve the accuracy and comprehensive efficiency of multiclass EEG signal recognition. The new joint approach used standardization for data preprocessing. Feature extraction was performed by combining FFT and principal component analysis methods. EEG signals were classified using the weighted k-nearest nenighbour method. In this study, experiments were conducted using public datasets of brainwave 0-9 digits classification. The result demonstrated that the accuracy and comprehensive efficiency of the novel joint method were 84% and 87%, respectively, which were better than those of the existing methods. The precision rate, recall rate, and F1 score of the novel joint method were 89%, 85%, and 0.85, respectively. In conclusion, the proposed joint method was effective in a complex scenario for multiclass EEG signal recognition.

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“…To proceed these problems, Picard et al [14] demonstrated that physiological signals are more pertinent than other modalities. In fact, they originate from the peripheral nervous system and central nervous system.…”

Section: Introductionmentioning

confidence: 99%

Emotion Classification in Arousal Valence Model using MAHNOB-HCI Database

Wiem¹,

Lachiri²

2017

ijacsa

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Abstract-Emotion recognition from physiological signals attracted the attention of researchers from different disciplines, such as affective computing, cognitive science and psychology. This paper aims to classify emotional statements using peripheral physiological signals based on arousal-valence evaluation. These signals are the Electrocardiogram, Respiration Volume, Skin Temperature and Galvanic Skin Response. We explored the signals collected in the MAHNOB-HCI multimodal tagging database. We defined the emotion into three different ways: two and three classes using 1-9 discrete self-rating scales and another model using 9 emotional keywords to establish the three defined areas in arousal-valence dimensions. To perform the accuracies, we began by removing the artefacts and noise from the signals, and then we extracted 169 features. We finished by classifying the emotional states using the support vector machine. The obtained results showed that the electrocardiogram and respiration volume were the most relevant signals for human's feeling recognition task. Moreover, the obtained accuracies were promising comparing to recent related works for each of the three establishments of emotion modeling.

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Automated estimation of human emotion from EEG using statistical features and SVM

Cited by 16 publications

References 7 publications

Class discovery from semi-structured EEG data for affective computing and personalisation

Class discovery from semi-structured EEG data for affective computing and personalisation

Novel joint algorithm based on EEG in complex scenarios

Emotion Classification in Arousal Valence Model using MAHNOB-HCI Database

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