NeuroSense: Short-term emotion recognition and understanding based on spiking neural network modelling of spatio-temporal EEG patterns

Tan, Clarence; Šarlija, Marko; Kasabov, Nikola

doi:10.1016/j.neucom.2020.12.098

Cited by 77 publications

(35 citation statements)

References 69 publications

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“…The next suggestion is to obtain new EEG datasets that involve older participants (age 50 or over) as a means of exploring emotional perception in different age groups. One practical application of this research is the creation of brain computer interface [4] towards developing human-computer interaction systems [61,62].…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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)

Self Cite

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“…Based on EEG segmentation for short-term change detection of facial markers, Tan et al (2021) constructed a subject related short-term EEG emotion recognition framework based on spiking neural network (SNN) and optimized super parameters of the data representation of pulse coding and dynamic evolving SNN (deSNN). The accuracy of valence and arousal classification on the DEAP dataset was 67.76 and 78.97%, respectively.…”

Section: Emotion Recognition Based On Database For Emotion Analysis Using Physiological Signalsmentioning

confidence: 99%

Review on Emotion Recognition Based on Electroencephalography

Liu

Zhang

et al. 2021

Front. Comput. Neurosci.

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Emotions are closely related to human behavior, family, and society. Changes in emotions can cause differences in electroencephalography (EEG) signals, which show different emotional states and are not easy to disguise. EEG-based emotion recognition has been widely used in human-computer interaction, medical diagnosis, military, and other fields. In this paper, we describe the common steps of an emotion recognition algorithm based on EEG from data acquisition, preprocessing, feature extraction, feature selection to classifier. Then, we review the existing EEG-based emotional recognition methods, as well as assess their classification effect. This paper will help researchers quickly understand the basic theory of emotion recognition and provide references for the future development of EEG. Moreover, emotion is an important representation of safety psychology.

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“…NeuCube is a BI-SNN, which was originally developed for modelling spatio-temporal data obtained from the brain but has since been used for a variety of applications, such as climate data modelling and stroke prediction. The architecture of NeuCube is shown in -Predicting brain re-wiring through mindfulness [63]; -Modelling neuroimaging data such as EEG and fMRI [62]; -Personalized brain data modelling [64]; -Emotion recognition [65]; -Speech, sound and music recognition [66]; -Moving object recognition [67]; -Prediction of events from temporal climate data (stroke) [64]; -Brain-computer interfaces (BCI) [68].…”

Section: The Neucube Architecturementioning

confidence: 99%

Spiking Neural Networks for Computational Intelligence: An Overview

Dora

Kasabov

2021

BDCC

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Deep neural networks with rate-based neurons have exhibited tremendous progress in the last decade. However, the same level of progress has not been observed in research on spiking neural networks (SNN), despite their capability to handle temporal data, energy-efficiency and low latency. This could be because the benchmarking techniques for SNNs are based on the methods used for evaluating deep neural networks, which do not provide a clear evaluation of the capabilities of SNNs. Particularly, the benchmarking of SNN approaches with regards to energy efficiency and latency requires realization in suitable hardware, which imposes additional temporal and resource constraints upon ongoing projects. This review aims to provide an overview of the current real-world applications of SNNs and identifies steps to accelerate research involving SNNs in the future.

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NeuroSense: Short-term emotion recognition and understanding based on spiking neural network modelling of spatio-temporal EEG patterns

Cited by 77 publications

References 69 publications

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

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

Review on Emotion Recognition Based on Electroencephalography

Spiking Neural Networks for Computational Intelligence: An Overview

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