Classification of emotion primitives from EEG signals using visual and audio stimuli

Daşdemir, Yaşar; Yıldırım, Serdar; Yıldırım, Esen

doi:10.1109/siu.2015.7130325

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…The compact support that is the range over which they are non-zero is [0, 2N − 1] and these waveforms could be implemented in db2, 4, 8 and 16; however, there is not a rule that we can follow to select the vanishing level, and the four vanishing moments were made through experimentation. The literature reports that most of the experimental proposals are based on complex prepossessing techniques, such as wavelet or matching pursuits techniques, to process the signals [4,28], and some other proposals analyze the signals without a pre-processing stage by using exhaustive methods instead of traditional digital signal processing [29,30].…”

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confidence: 99%

Emotion Recognition by Correlating Facial Expressions and EEG Analysis

et al. 2021

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Emotion recognition is a fundamental task that any affective computing system must perform to adapt to the user’s current mood. The analysis of electroencephalography signals has gained notoriety in studying human emotions because of its non-invasive nature. This paper presents a two-stage deep learning model to recognize emotional states by correlating facial expressions and brain signals. Most of the works related to the analysis of emotional states are based on analyzing large segments of signals, generally as long as the evoked potential lasts, which could cause many other phenomena to be involved in the recognition process. Unlike with other phenomena, such as epilepsy, there is no clearly defined marker of when an event begins or ends. The novelty of the proposed model resides in the use of facial expressions as markers to improve the recognition process. This work uses a facial emotion recognition technique (FER) to create identifiers each time an emotional response is detected and uses them to extract segments of electroencephalography (EEG) records that a priori will be considered relevant for the analysis. The proposed model was tested on the DEAP dataset.

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Emotion Recognition by Correlating Facial Expressions and EEG Analysis

et al. 2021

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“…Thus far, EEG has been effectively used for assessing brain activity with specific performance in comprehension of cognitive patterns related to neurological and mental issues [17][18][19][20][21]. In [22], an emotion recognition experiment was conducted with respect to the impacts of audio and visual stimuli on 25 individuals' EEG brainwaves. The accuracy of emotion identification was 78% for valence film clips and 82% for an arousal state.…”

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confidence: 99%

Emotion Recognition and Understanding Using EEG Data in A Brain-Inspired Spiking Neural Network Architecture

Alzhrani

Doborjeh

et al. 2021

2021 International Joint Conference on Neural Networks (IJCNN)

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This paper is in the scope of emotion recognition by employing a recurrent spiking neural network (BI-SNN) architecture for modelling, mapping, learning, classifying, visualising, and understanding of spatio-temporal Electroencephalogram (EEG) data related to different emotional states. It further explores, develops, and applies a methodology based on the NeuCube BI-SNN, that includes methods for EEG data encoding, data mapping into a 3-dimensional BI-SNN model, unsupervised learning using spike-timing dependent plasticity (STDP) rule, spike-driven supervised learning, output classification, network analysis, and model visualisation and interpretation. The research conducted to model different emotional subtypes through mapping both space (brain regions) and time (brain dynamics) components of EEG brain data into SNN architecture. Here, a benchmark EEG dataset was used to design an empirical study that consisted of different experiments for classification of emotions. The obtained accuracy of 94.83% for EEG classification of four types of emotions was superior when compared with traditional machine learning techniques. The BI-SNN models not only detected the brain activity patterns related to positive and negative emotions with a high accuracy, but also revealed new knowledge about the brain areas activated in relation to different emotions. The research confirmed that neural activation increased in the frontal sites of brain (F7, F3, AF4) associated with positive emotions, while in the case of the negative emotions, connectivity strength was concentrated in the frontal (F4, AF3, F7, F8) and parietal sites of the brain (P7, P8).

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