Recognition of psychological emotion by EEG features

Huang, Chunyuan

doi:10.1007/s13721-020-00283-2

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…Later, Keelawat et al (2021) acquired EEG data based on a Waveguard EEG cap with a 250 Hz sampling rate from 12 students from Osaka University, to whom song samples were presented. What’s more, to effectively collect EEG signals, the attachment position of electrodes for EEG equipment in many studies follows the international 10–20 system ( Chai et al, 2016 ; Seo et al, 2019 ; Hou et al, 2020 ; Huang, 2021 ).…”

Section: Acquisition Of Electroencephalography Signals For Emotion Recognitionmentioning

confidence: 99%

Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review

Cai

Xiao

Cui

et al. 2021

Front. Syst. Neurosci.

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Emotion recognition has become increasingly prominent in the medical field and human-computer interaction. When people’s emotions change under external stimuli, various physiological signals of the human body will fluctuate. Electroencephalography (EEG) is closely related to brain activity, making it possible to judge the subject’s emotional changes through EEG signals. Meanwhile, machine learning algorithms, which are good at digging out data features from a statistical perspective and making judgments, have developed by leaps and bounds. Therefore, using machine learning to extract feature vectors related to emotional states from EEG signals and constructing a classifier to separate emotions into discrete states to realize emotion recognition has a broad development prospect. This paper introduces the acquisition, preprocessing, feature extraction, and classification of EEG signals in sequence following the progress of EEG-based machine learning algorithms for emotion recognition. And it may help beginners who will use EEG-based machine learning algorithms for emotion recognition to understand the development status of this field. The journals we selected are all retrieved from the Web of Science retrieval platform. And the publication dates of most of the selected articles are concentrated in 2016–2021.

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Section: Acquisition Of Electroencephalography Signals For Emotion Recognitionmentioning

confidence: 99%

Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review

Cai

Xiao

Cui

et al. 2021

Front. Syst. Neurosci.

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“…By understanding the emotional content of a text, businesses can tailor their products and services to meet consumer needs better [5]. Emotional computing, as a field, seeks to bridge the gap between human emotions and computer systems, enabling machines to understand, respond to, and generate dynamic content [6]. In the case of Chinese, this involves linguistic challenges to cultural and contextual considerations unique to the Chinese-speaking world [7].…”

Section: Introductionmentioning

confidence: 99%

Research on Emotional Analysis and Emotional Color Extraction Algorithm of Chinese Language and Literature Based on Emotional Computing

Lan Wang

2024

jes

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In China, the emotional response of diverse cultural bearers, including literature, movies, and other media, frequently mirrors the country's cultural improvement. Consequently, emotional analysis of the text and paintings may be used to examine the cultural evolution, determine its growth dynamics, and sort out its context. The project aims to investigate how well sophisticated affective computing performs in identifying and analyzing the emotional aspects of traditional Chinese literature and language. The study selects the Double Channel based Self-organized Residual Network-50 V2 (DC-SoResNet 50V2) algorithm based on a combination of deep learning algorithms and further improving the (DC-SoResNet 50V2) algorithm using Emperor penguin Colony optimizer (EPCO). Moreover, the color image and text features from traditional Chinese literature are extracted using hummingbird-based term frequency-inverse document frequency (HB-TF-IDF) and Stochastic Color Harmony Algorithm (SCHA). Comparisons are made between the proposed and standard approaches using the training and test sets, respectively. The training time of the proposed approach is stable at around 25.1 s, while the test time is steady at about 18.5 s. The emotion identification accuracy of the proposed method achieved average of 99.11 and 99.23% in testing and training sets for the entire datasets, respectively. Comprehensive analysis of classic literary works can be possible with the help of this research, which offers fresh viewpoints and theoretical references.

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“…In recent years, many researchers have used EEG to automatically recognize human emotions and obtained high recognition accuracy ( Kurniawan et al, 2013 ; Luo et al, 2018 ; Qing et al, 2019 ; Torres et al, 2020 ; Gao et al, 2021 ; Huang, 2021 ; Liu et al, 2021 ; Sharma et al, 2021 ; Yedukondalu and Sharma, 2022 ). In Table 1 , the methods and accuracy rates of researchers using EEG to diagnose depression are shown.…”

Section: Introductionmentioning

confidence: 99%

EEG diagnosis of depression based on multi-channel data fusion and clipping augmentation and convolutional neural network

Wang

Kang²,

Huo³

et al. 2022

Front. Physiol.

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Depression is an undetectable mental disease. Most of the patients with depressive symptoms do not know that they are suffering from depression. Since the novel Coronavirus pandemic 2019, the number of patients with depression has increased rapidly. There are two kinds of traditional depression diagnosis. One is that professional psychiatrists make diagnosis results for patients, but it is not conducive to large-scale depression detection. Another is to use electroencephalography (EEG) to record neuronal activity. Then, the features of the EEG are extracted using manual or traditional machine learning methods to diagnose the state and type of depression. Although this method achieves good results, it does not fully utilize the multi-channel information of EEG. Aiming at this problem, an EEG diagnosis method for depression based on multi-channel data fusion cropping enhancement and convolutional neural network is proposed. First, the multi-channel EEG data are transformed into 2D images after multi-channel fusion (MCF) and multi-scale clipping (MSC) augmentation. Second, it is trained by a multi-channel convolutional neural network (MCNN). Finally, the trained model is loaded into the detection device to classify the input EEG signals. The experimental results show that the combination of MCF and MSC can make full use of the information contained in the single sensor records, and significantly improve the classification accuracy and clustering effect of depression diagnosis. The method has the advantages of low complexity and good robustness in signal processing and feature extraction, which is beneficial to the wide application of detection systems.

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Recognition of psychological emotion by EEG features

Cited by 8 publications

References 27 publications

Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review

Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review

Research on Emotional Analysis and Emotional Color Extraction Algorithm of Chinese Language and Literature Based on Emotional Computing

EEG diagnosis of depression based on multi-channel data fusion and clipping augmentation and convolutional neural network

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