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Статті в журналах з теми "DEAP EEG"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

Oka, Hayato, Keiko Ono, and Adamidis Panagiotis. "Attention-Based PSO-LSTM for Emotion Estimation Using EEG." Sensors 24, no. 24 (2024): 8174. https://doi.org/10.3390/s24248174.

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Анотація:

Recent advances in emotion recognition through Artificial Intelligence (AI) have demonstrated potential applications in various fields (e.g., healthcare, advertising, and driving technology), with electroencephalogram (EEG)-based approaches demonstrating superior accuracy compared to facial or vocal methods due to their resistance to intentional manipulation. This study presents a novel approach to enhance EEG-based emotion estimation accuracy by emphasizing temporal features and efficient parameter space exploration. We propose a model combining Long Short-Term Memory (LSTM) with an attention

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2

Anjum, Madiha, Wardah Batool, Raazia Saher, and Sanay Muhammad Umar Saeed. "Enhanced Classification of Video-Evoked Stress Response Using Power Spectral Density Features." Applied Sciences 14, no. 20 (2024): 9527. http://dx.doi.org/10.3390/app14209527.

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Анотація:

The analysis of stress in response to videos using electroencephalography (EEG) has emerged as a significant field of research. In this study, we propose a methodology for classifying stress responses to videos using the Database for Emotion Analysis using Physiological Signals (DEAP). EEG signals are preprocessed with resampling and a median filter. We extracted Power Spectral Density (PSD) features from the alpha, beta, delta, and theta bands of the preprocessed EEG. Instances were labeled based on the valence and arousal values provided in the DEAP dataset in response to the presented video

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3

Mahmoud, Aseel, Khalid Amin, Mohamad Mahmoud Al Rahhal, Wail S. Elkilani, Mohamed Lamine Mekhalfi, and Mina Ibrahim. "A CNN Approach for Emotion Recognition via EEG." Symmetry 15, no. 10 (2023): 1822. http://dx.doi.org/10.3390/sym15101822.

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Анотація:

Emotion recognition via electroencephalography (EEG) has been gaining increasing attention in applications such as human–computer interaction, mental health assessment, and affective computing. However, it poses several challenges, primarily stemming from the complex and noisy nature of EEG signals. Commonly adopted strategies involve feature extraction and machine learning techniques, which often struggle to capture intricate emotional nuances and may require extensive handcrafted feature engineering. To address these limitations, we propose a novel approach utilizing convolutional neural net

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4

Kanchi, Lohitha Lakshmi, Chandana Sri Narra, Guna Mantri, Madhupriya Palepogu, and Chandra Sekhar Reddy Mettu. "Emotion Recognition from Brain EEG Signals." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 2036–43. http://dx.doi.org/10.22214/ijraset.2024.59255.

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Анотація:

Abstract: The recognition of emotions plays a vital role in various fields such as neuroscience, cognitive sciences, and biomedical engineering. This particular project is centered on the development of a system for recognizing emotions through EEG signals. The main goal is to accurately classify different emotional states like valence and arousal by analyzing EEG brain wave patterns. The study is based on the DEAP dataset, which contains EEG and peripheral physiological signals recorded as participants interacted with video clips and music. The main objective is to explore and compare the eff

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5

Kulkarni, Deepthi D., Vaibhav Vitthalrao Dixit, and Shweta Shirish Deshmukh. "Emotion detection using EEG: hybrid classification approach." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 1 (2024): 459. http://dx.doi.org/10.11591/ijeecs.v35.i1.pp459-466.

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Анотація:

The field of emotion research facilitates the development of several applications, all of which aim to precisely and swiftly identify emotions. Speech and facial expressions are the main focus of typical emotion analysis, although they are not accurate indicators of true feelings. Signal analysis, namely the electroencephalograph (EEG) of the brain signals, is the other area in which emotions are analyzed. When compared to other modalities, EEG offers precise and comprehensive data that facilitates the estimation of emotional states. In order to categories the emotions using an EEG signal, thi

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6

Deepthi, D. Kulkarni Vaibhav Vitthalrao Dixit Shweta Shirish Deshmukh. "Emotion detection using EEG: hybrid classification approach." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 1 (2024): 459–66. https://doi.org/10.11591/ijeecs.v35.i1.pp459-466.

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Анотація:

The field of emotion research facilitates the development of several applications, all of which aim to precisely and swiftly identify emotions. Speech and facial expressions are the main focus of typical emotion analysis, although they are not accurate indicators of true feelings. Signal analysis, namely the electroencephalograph (EEG) of the brain signals, is the other area in which emotions are analyzed. When compared to other modalities, EEG offers precise and comprehensive data that facilitates the estimation of emotional states. In order to categories the emotions using an EEG signal, thi

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7

Angreni, Ni Putu Dewi, Agus Muliantara, and Yuriko Christian. "Quantization-Based Novel Extraction Method Of EEG Signal For Classification." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 9, no. 2 (2020): 169. http://dx.doi.org/10.24843/jlk.2020.v09.i02.p02.

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Анотація:

In the pattern recognition field, features or object’s characteristics are one of the key points to recognizing them. The feature extraction process will see that objects have different features, where the features are obtained through the analysis process from the extractor, such as for data statistics, energy, power spectral, and so on. This study aims to enrich the point of view of EEG signal features by quantifying the signal. It will be analyzed whether the features obtained by quantization represent the EEG signal object from different viewpoints. This research uses the DEAP dataset, wit

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8

Xu, Chang, Hong Liu, and Wei Qi. "EEG Emotion Recognition Based on Federated Learning Framework." Electronics 11, no. 20 (2022): 3316. http://dx.doi.org/10.3390/electronics11203316.

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Анотація:

Emotion recognition based on the multi-channel electroencephalograph (EEG) is becoming increasingly attractive. However, the lack of large datasets and privacy concerns lead to models that often do not have enough data for training, limiting the research and application of Deep Learn (DL) methods in this direction. At present, the popular federated learning (FL) approach, which can collaborate with different clients to perform distributed machine learning without sending data to a central server, provides a solution to the above problem. In this paper, we extended the FL method to the field of

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9

Bouazizi, Samar, Emna Benmohamed, and Hela Ltifi. "Enhancing EEG-based emotion recognition using PSD-Grouped Deep Echo State Network." JUCS - Journal of Universal Computer Science 29, no. 10 (2023): 1116–38. http://dx.doi.org/10.3897/jucs.98789.

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Анотація:

Emotions are a crucial aspect of daily life and play a vital role in shaping human inter-actions. The purpose of this paper is to introduce a novel approach to recognize human emotions through the use of electroencephalogram (EEG) signals. To recognize these signals for emotion prediction, we employ a paradigm of Reservoir Computing (RC), called Echo State Network (ESN). In our analysis, we focus on two specific classes of emotion recognition: H/L Arousal and H/L Valence. We suggest using the Deep ESN model in conjunction with the Welch Power Spectral Density (Wlech PSD) method for emotion cla

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10

Bouazizi, Samar, Emna Benmohamed, and Hela Ltifi. "Enhancing EEG-based emotion recognition using PSD-Grouped Deep Echo State Network." JUCS - Journal of Universal Computer Science 29, no. (10) (2023): 1116–38. https://doi.org/10.3897/jucs.98789.

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Анотація:

Emotions are a crucial aspect of daily life and play a vital role in shaping human inter-actions. The purpose of this paper is to introduce a novel approach to recognize human emotions through the use of electroencephalogram (EEG) signals. To recognize these signals for emotion prediction, we employ a paradigm of Reservoir Computing (RC), called Echo State Network (ESN). In our analysis, we focus on two specific classes of emotion recognition: H/L Arousal and H/L Valence. We suggest using the Deep ESN model in conjunction with the Welch Power Spectral Density (Wlech PSD) method for emotion cla

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11

Kanuboyina, V. Satyanarayana Naga, T. Shankar, and Rama Raju Venkata Penmetsa. "Electroencephalography based human emotion state classification using principal component analysis and artificial neural network." Multiagent and Grid Systems 18, no. 3-4 (2023): 263–78. http://dx.doi.org/10.3233/mgs-220333.

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Анотація:

In recent decades, the automatic emotion state classification is an important technology for human-machine interactions. In Electroencephalography (EEG) based emotion classification, most of the existing methodologies cannot capture the context information of the EEG signal and ignore the correlation information between dissimilar EEG channels. Therefore, in this study, a deep learning based automatic method is proposed for effective emotion state classification. Firstly, the EEG signals were acquired from the real time and databases for emotion analysis using physiological signals (DEAP), and

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12

Aldawsari, Haya, Saad Al-Ahmadi, and Farah Muhammad. "Optimizing 1D-CNN-Based Emotion Recognition Process through Channel and Feature Selection from EEG Signals." Diagnostics 13, no. 16 (2023): 2624. http://dx.doi.org/10.3390/diagnostics13162624.

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Анотація:

EEG-based emotion recognition has numerous real-world applications in fields such as affective computing, human-computer interaction, and mental health monitoring. This offers the potential for developing IOT-based, emotion-aware systems and personalized interventions using real-time EEG data. This study focused on unique EEG channel selection and feature selection methods to remove unnecessary data from high-quality features. This helped improve the overall efficiency of a deep learning model in terms of memory, time, and accuracy. Moreover, this work utilized a lightweight deep learning meth

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13

Ayesh, Aladdin, Miguel Arevalillo-Herra´ez, and Pablo Arnau-González. "SOM-Based Class Discovery for Emotion Detection Based on DEAP Dataset." International Journal of Software Science and Computational Intelligence 10, no. 1 (2018): 15–26. http://dx.doi.org/10.4018/ijssci.2018010102.

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Анотація:

This paper investigates the possibility of identifying classes by clustering. This study includes employing Self-Organizing Maps (SOM) in identifying clusters from EEG signals that could then be mapped to emotional classes. Beginning by training varying sizes of SOM with the EEG data provided from the public dataset: DEAP. The produced graphs showing Neighbor Distance, Sample Hits, and Weight Position are examined. Following that, the ground-truth label provided in DEAP is tested, in order to identify correlations between the label and the clusters produced by the SOM. The results show that th

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14

Jin, Longbin, and Eun Yi Kim. "Interpretable Cross-Subject EEG-Based Emotion Recognition Using Channel-Wise Features." Sensors 20, no. 23 (2020): 6719. http://dx.doi.org/10.3390/s20236719.

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Анотація:

Electroencephalogram (EEG)-based emotion recognition is receiving significant attention in research on brain-computer interfaces (BCI) and health care. To recognize cross-subject emotion from EEG data accurately, a technique capable of finding an effective representation robust to the subject-specific variability associated with EEG data collection processes is necessary. In this paper, a new method to predict cross-subject emotion using time-series analysis and spatial correlation is proposed. To represent the spatial connectivity between brain regions, a channel-wise feature is proposed, whi

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15

Thuong, Duong Thi Mai, Nguyen Phuong Huy, Trung-Nghia Phung, and Dang Ngoc Cuong. "A Lightweight Deep Learning Network for Emotion Recognition Applications on Portable Devices." International Journal of Knowledge and Systems Science 16, no. 1 (2025): 1–23. https://doi.org/10.4018/ijkss.373712.

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Анотація:

The search for efficient deep learning architectures for emotion recognition using EEG signals has drawn great interest due to applications in healthcare, education, and intelligent interaction. These models must meet three key requirements: achieving high accuracy with fewer electrodes (32, 14, or even 5), maintaining stable performance across frequency bands, and being lightweight enough for deployment on low-resource devices. This paper proposes EEG_SICNET, an enhanced 1D-CNN integrated with Squeeze and Excitation and Inception blocks to optimize EEG signal processing. Experiments on DEAP,

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16

Wu, Xun, Wei-Long Zheng, Ziyi Li, and Bao-Liang Lu. "Investigating EEG-based functional connectivity patterns for multimodal emotion recognition." Journal of Neural Engineering 19, no. 1 (2022): 016012. http://dx.doi.org/10.1088/1741-2552/ac49a7.

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Abstract Objective. Previous studies on emotion recognition from electroencephalography (EEG) mainly rely on single-channel-based feature extraction methods, which ignore the functional connectivity between brain regions. Hence, in this paper, we propose a novel emotion-relevant critical subnetwork selection algorithm and investigate three EEG functional connectivity network features: strength, clustering coefficient, and eigenvector centrality. Approach. After constructing the brain networks by the correlations between pairs of EEG signals, we calculated critical subnetworks through the avera

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17

Jon, Hyo Jin, Longbin Jin, Hyuntaek Jung, Hyunseo Kim, and Eun Yi Kim. "EEG-RegNet: Regressive Emotion Recognition in Continuous VAD Space Using EEG Signals." Mathematics 13, no. 1 (2024): 87. https://doi.org/10.3390/math13010087.

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Анотація:

Electroencephalogram (EEG)-based emotion recognition has garnered significant attention in brain–computer interface research and healthcare applications. While deep learning models have been extensively studied, most are designed for classification tasks and struggle to accurately predict continuous emotional scores in regression settings. In this paper, we introduce EEG-RegNet, a novel deep neural network tailored for precise emotional score prediction across the continuous valence–arousal–dominance (VAD) space. EEG-RegNet tackles two core challenges: extracting subject-independent, emotion-r

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18

Xiong, Fan, Mengzhao Fan, Xu Yang, Chenxiao Wang, and Jinli Zhou. "Research on emotion recognition using sparse EEG channels and cross-subject modeling based on CNN-KAN-F2CA model." PLOS One 20, no. 5 (2025): e0322583. https://doi.org/10.1371/journal.pone.0322583.

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Анотація:

Emotion recognition plays a significant role in artificial intelligence and human-computer interaction. Electroencephalography (EEG) signals, due to their ability to directly reflect brain activity, have become an essential tool in emotion recognition research. However, the low dimensionality of sparse EEG channel data presents a key challenge in extracting effective features. This paper proposes a sparse channel EEG-based emotion recognition method using the CNN-KAN-F2CA network to address the challenges of limited feature extraction and cross-subject variability in emotion recognition. Throu

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19

Lv, Ziyi, Jing Zhang, and Estanislao Epota Oma. "A Novel Method of Emotion Recognition from Multi-Band EEG Topology Maps Based on ERENet." Applied Sciences 12, no. 20 (2022): 10273. http://dx.doi.org/10.3390/app122010273.

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Анотація:

EEG-based emotion recognition research has become a hot research topic. However, many studies focus on identifying emotional states from time domain features, frequency domain features, and time-frequency domain features of EEG signals, ignoring the spatial information and frequency band characteristics of the EEG signals. In this paper, an emotion recognition method based on multi-band EEG topology maps is proposed by combining the frequency domain features, spatial information, and frequency band characteristics of multi-channel EEG signals. In this method, multi-band EEG topology maps are i

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20

Yang, Heekyung, Jongdae Han, and Kyungha Min. "A Multi-Column CNN Model for Emotion Recognition from EEG Signals." Sensors 19, no. 21 (2019): 4736. http://dx.doi.org/10.3390/s19214736.

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Анотація:

We present a multi-column CNN-based model for emotion recognition from EEG signals. Recently, a deep neural network is widely employed for extracting features and recognizing emotions from various biosignals including EEG signals. A decision from a single CNN-based emotion recognizing module shows improved accuracy than the conventional handcrafted feature-based modules. To further improve the accuracy of the CNN-based modules, we devise a multi-column structured model, whose decision is produced by a weighted sum of the decisions from individual recognizing modules. We apply the model to EEG

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21

Sarikaya, Mehmet Ali, and Gökhan Ince. "Improved BCI calibration in multimodal emotion recognition using heterogeneous adversarial transfer learning." PeerJ Computer Science 11 (January 20, 2025): e2649. https://doi.org/10.7717/peerj-cs.2649.

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Анотація:

The use of brain-computer interface (BCI) technology to identify emotional states has gained significant interest, especially with the rise of virtual reality (VR) applications. However, the extensive calibration required for precise emotion recognition models presents a significant challenge, particularly for sensitive groups such as children, elderly, and patients. This study presents a novel approach that utilizes heterogeneous adversarial transfer learning (HATL) to synthesize electroencephalography (EEG) data from various other signal modalities, reducing the need for lengthy calibration

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22

S. Jerritta, Muhammadu Sathik Raja M. S. ,. "Stress-Nets- A Novel LSTM Ensembled Single Feed Forward Layers for Stress Classification with EEG Signals." Journal of Electrical Systems 20, no. 2s (2024): 256–72. http://dx.doi.org/10.52783/jes.1136.

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Анотація:

Mental instability and emotional imbalance of the individual can be reflected in the form of stress which results in an inappropriate work ethics. There are various methods for the Stress creation. Moreover , several bio-signal sources such as Electroencephalograph(EEG), Electrocardiography(ECG) and Electromyography(EMG) are considered to be the important catalyst for developing the stress detection systems(SDS). Recently, machine and deep learning algorithms has gained more popularity in designing the SDS using the bio-signals. Further, EEG based SDS with ML and DL algorithms plays an importa

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23

Hu, Wanrou, Gan Huang, Linling Li, Li Zhang, Zhiguo Zhang, and Zhen Liang. "Video‐triggered EEG‐emotion public databases and current methods: A survey." Brain Science Advances 6, no. 3 (2020): 255–87. http://dx.doi.org/10.26599/bsa.2020.9050026.

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Анотація:

Emotions, formed in the process of perceiving external environment, directly affect human daily life, such as social interaction, work efficiency, physical wellness, and mental health. In recent decades, emotion recognition has become a promising research direction with significant application values. Taking the advantages of electroencephalogram (EEG) signals (i.e., high time resolution) and video‐based external emotion evoking (i.e., rich media information), video‐triggered emotion recognition with EEG signals has been proven as a useful tool to conduct emotion‐related studies in a laborator

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24

Lin, Juan, Guoqiang Ma, Binhao Li, Tianyang Yu, and Yanjun Li. "Research on Emotional Recognition System of EEG Signals Based on CNN+LSTM." Advances in Computer and Materials Scienc Research 1, no. 1 (2024): 231. http://dx.doi.org/10.70114/acmsr.2024.1.1.p231.

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We studied an emotion recognition system based on EEG signals, conducted MATLAB simulation for EEG signal collection, and conducted experiments on EEG signal collection using STM32 microcontroller. We proposed an EEG emotion recognition system based on convolutional neural network (CNN) and short-term memory artificial neural network (LSTM) models. The model used gradient descent algorithm and cross entropy loss function algorithm, and was used the DEAP dataset to verify the accuracy of emotion recognition results, which reached 94.023%, The accuracy of emotion recognition results which is ach

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25

Abdumalikov, Sherzod, Jingeun Kim, and Yourim Yoon. "Performance Analysis and Improvement of Machine Learning with Various Feature Selection Methods for EEG-Based Emotion Classification." Applied Sciences 14, no. 22 (2024): 10511. http://dx.doi.org/10.3390/app142210511.

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Анотація:

Emotion classification is a challenge in affective computing, with applications ranging from human–computer interaction to mental health monitoring. In this study, the classification of emotional states using electroencephalography (EEG) data were investigated. Specifically, the efficacy of the combination of various feature selection methods and hyperparameter tuning of machine learning algorithms for accurate and robust emotion recognition was studied. The following feature selection methods were explored: filter (SelectKBest with analysis of variance (ANOVA) F-test), embedded (least absolut

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26

Yang, Haihui, Shiguo Huang, Shengwei Guo, and Guobing Sun. "Multi-Classifier Fusion Based on MI–SFFS for Cross-Subject Emotion Recognition." Entropy 24, no. 5 (2022): 705. http://dx.doi.org/10.3390/e24050705.

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Анотація:

With the widespread use of emotion recognition, cross-subject emotion recognition based on EEG signals has become a hot topic in affective computing. Electroencephalography (EEG) can be used to detect the brain’s electrical activity associated with different emotions. The aim of this research is to improve the accuracy by enhancing the generalization of features. A Multi-Classifier Fusion method based on mutual information with sequential forward floating selection (MI_SFFS) is proposed. The dataset used in this paper is DEAP, which is a multi-modal open dataset containing 32 EEG channels and

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27

Abdel-Ghaffar, Eman A., and May Salama. "The Effect of Stress on a Personal Identification System Based on Electroencephalographic Signals." Sensors 24, no. 13 (2024): 4167. http://dx.doi.org/10.3390/s24134167.

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Анотація:

Personal identification systems based on electroencephalographic (EEG) signals have their own strengths and limitations. The stability of EEG signals strongly affects such systems. The human emotional state is one of the important factors that affects EEG signals’ stability. Stress is a major emotional state that affects individuals’ capability to perform day-to-day tasks. The main objective of this work is to study the effect of mental and emotional stress on such systems. Two experiments have been performed. In the first, we used hand-crafted features (time domain, frequency domain, and non-

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28

Wu, Xia, Yumei Zhang, Jingjing Li, Honghong Yang, and Xiaojun Wu. "FC-TFS-CGRU: A Temporal–Frequency–Spatial Electroencephalography Emotion Recognition Model Based on Functional Connectivity and a Convolutional Gated Recurrent Unit Hybrid Architecture." Sensors 24, no. 6 (2024): 1979. http://dx.doi.org/10.3390/s24061979.

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Анотація:

The gated recurrent unit (GRU) network can effectively capture temporal information for 1D signals, such as electroencephalography and event-related brain potential, and it has been widely used in the field of EEG emotion recognition. However, multi-domain features, including the spatial, frequency, and temporal features of EEG signals, contribute to emotion recognition, while GRUs show some limitations in capturing frequency–spatial features. Thus, we proposed a hybrid architecture of convolutional neural networks and GRUs (CGRU) to effectively capture the complementary temporal features and

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29

Chao, Hao, Liang Dong, Yongli Liu, and Baoyun Lu. "Emotion Recognition from Multiband EEG Signals Using CapsNet." Sensors 19, no. 9 (2019): 2212. http://dx.doi.org/10.3390/s19092212.

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Анотація:

Emotion recognition based on multi-channel electroencephalograph (EEG) signals is becoming increasingly attractive. However, the conventional methods ignore the spatial characteristics of EEG signals, which also contain salient information related to emotion states. In this paper, a deep learning framework based on a multiband feature matrix (MFM) and a capsule network (CapsNet) is proposed. In the framework, the frequency domain, spatial characteristics, and frequency band characteristics of the multi-channel EEG signals are combined to construct the MFM. Then, the CapsNet model is introduced

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30

Du, Yuxiao, Han Ding, Min Wu, Feng Chen, and Ziman Cai. "MES-CTNet: A Novel Capsule Transformer Network Base on a Multi-Domain Feature Map for Electroencephalogram-Based Emotion Recognition." Brain Sciences 14, no. 4 (2024): 344. http://dx.doi.org/10.3390/brainsci14040344.

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Анотація:

Emotion recognition using the electroencephalogram (EEG) has garnered significant attention within the realm of human–computer interaction due to the wealth of genuine emotional data stored in EEG signals. However, traditional emotion recognition methods are deficient in mining the connection between multi-domain features and fitting their advantages. In this paper, we propose a novel capsule Transformer network based on a multi-domain feature for EEG-based emotion recognition, referred to as MES-CTNet. The model’s core consists of a multichannel capsule neural network(CapsNet) embedded with E

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31

Li, Zhengping, Hongsheng Xia, and Lijun Wang. "EEG Emotion Classification and Correlation Research Based on Deep Learning." Journal of Physics: Conference Series 2384, no. 1 (2022): 012043. http://dx.doi.org/10.1088/1742-6596/2384/1/012043.

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Анотація:

Abstract In the classification of EEG emotions, the improvement of the generalization performance of deep learning networks is particularly important for the information extraction of EEG raw signals. In this paper, we use label smoothing (LS) as a regularization method to improve the generalization performance of the network by alleviating the overfitting of the network. The method is evaluated on the DEAP (Database for Emotion Analysis using Physiological signals) dataset, and the differences in emotion classification in different brain regions are compared. The experimental results show tha

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32

Sushma, S., S. Venkat, K. Mohanavelu, Jac A. R. Fredo, and T. Christy Bobby. "EEG-Based User Identification using Machine Learning and Deep Learning Approaches." Current Directions in Biomedical Engineering 10, no. 4 (2024): 639–44. https://doi.org/10.1515/cdbme-2024-2157.

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Анотація:

Abstract In recent years, Electroencephalogram (EEG) based user authentication systems have gained significant interest as an innovative approach for identity verification. EEGs are considered to be a novel biometric attribute due to the individuality of each person’s cerebral activity patterns. This work explores the feasibility and efficiency of utilizing EEG signals, generated in response to emotional stimuli, for user authentication applications, by implementing Machine Learning (ML) and Deep Learning (DL) approaches. Support Vector Machine (SVM), Random Forest (RF) classifier and 1D Convo

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Asghar, Muhammad Adeel, Muhammad Jamil Khan, Fawad, et al. "EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach." Sensors 19, no. 23 (2019): 5218. http://dx.doi.org/10.3390/s19235218.

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Анотація:

Much attention has been paid to the recognition of human emotions with the help of electroencephalogram (EEG) signals based on machine learning technology. Recognizing emotions is a challenging task due to the non-linear property of the EEG signal. This paper presents an advanced signal processing method using the deep neural network (DNN) for emotion recognition based on EEG signals. The spectral and temporal components of the raw EEG signal are first retained in the 2D Spectrogram before the extraction of features. The pre-trained AlexNet model is used to extract the raw features from the 2D

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Cimtay, Yucel, and Erhan Ekmekcioglu. "Investigating the Use of Pretrained Convolutional Neural Network on Cross-Subject and Cross-Dataset EEG Emotion Recognition." Sensors 20, no. 7 (2020): 2034. http://dx.doi.org/10.3390/s20072034.

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Анотація:

The electroencephalogram (EEG) has great attraction in emotion recognition studies due to its resistance to deceptive actions of humans. This is one of the most significant advantages of brain signals in comparison to visual or speech signals in the emotion recognition context. A major challenge in EEG-based emotion recognition is that EEG recordings exhibit varying distributions for different people as well as for the same person at different time instances. This nonstationary nature of EEG limits the accuracy of it when subject independency is the priority. The aim of this study is to increa

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Jiménez-Guarneros, Magdiel, and Roberto Alejo-Eleuterio. "A Class-Incremental Learning Method Based on Preserving the Learned Feature Space for EEG-Based Emotion Recognition." Mathematics 10, no. 4 (2022): 598. http://dx.doi.org/10.3390/math10040598.

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Анотація:

Deep learning-based models have shown to be one of the main active research topics in emotion recognition systems from Electroencephalogram (EEG) signals. However, a significant challenge is to effectively recognize new emotions that are incorporated sequentially, as current models must perform retraining from scratch. In this paper, we propose a Class-Incremental Learning (CIL) method, named Incremental Learning preserving the Learned Feature Space (IL2FS), in order to enable deep learning models to incorporate new emotions (classes) into the already known. IL2FS performs a weight aligning to

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Patil, Sangita Ajit, and Ajay Namdeorao Paithane. "AI-Driven Multimodal Stress Detection: A Comparative Study." Biomedical and Pharmacology Journal 18, December Spl Edition (2025): 245–55. https://doi.org/10.13005/bpj/3085.

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Анотація:

Stress affects mental and physical health, contributing to cardiovascular diseases and cognitive disorders, and early detection plays a crucial role in mitigating these risks. This study enhances stress detection by analyzing electroencephalography (EEG) signals from the DEAP ( A Database using Physiological Signals) data set and electrocardiogram (ECG) signals from the WESAD (Wearable Stress and Affect Detection) data set, with EEG offering a cost-effective solution and ECG providing detailed cardiovascular insights. It compares individual sensor analysis with multi-sensor fusion, demonstrati

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Patil, Sangita Ajit, and Ajay N. Paithane. "Optimized EEG-Based Stress Detection: A Novel Approach." Biomedical and Pharmacology Journal 17, no. 4 (2024): 2607–16. https://doi.org/10.13005/bpj/3052.

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Анотація:

Mental stress from tight deadlines and financial worries often causes both mental and physical health issues, affecting productivity and decision-making. This study aims to improve stress detection by analyzing EEG signals, which provide a cost-effective, non-invasive method for tracking brain activity. Recent stress detection systems face challenges such as computational complexity, noisy data, and high dimensionality. This study introduces optimal feature selection in an EEG-based stress detection system using the Archimedes Optimization Algorithm (AOA) and Analytical Hierarchical Process (A

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Zhang, Xiaodan, Shuyi Wang, Kemeng Xu, Rui Zhao, and Yichong She. "Cross-subject EEG-based emotion recognition through dynamic optimization of random forest with sparrow search algorithm." Mathematical Biosciences and Engineering 21, no. 3 (2024): 4779–800. http://dx.doi.org/10.3934/mbe.2024210.

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Анотація:

<abstract> <p>The objective of EEG-based emotion recognition is to classify emotions by decoding signals, with potential applications in the fields of artificial intelligence and bioinformatics. Cross-subject emotion recognition is more difficult than intra-subject emotion recognition. The poor adaptability of classification model parameters is a significant factor of low accuracy in cross-subject emotion recognition. We propose a model of a dynamically optimized Random Forest based on the Sparrow Search Algorithm (SSA-RF). The decision trees number (DTN) and the leave minimum numb

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Chao, Hao, and Fang Yuan. "EEG Emotion Recognition based on Multi scale Self Attention Convolutional Networks." EAI Endorsed Transactions on e-Learning 8, no. 4 (2023): e4. http://dx.doi.org/10.4108/eetel.3722.

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Анотація:

A multi-view self-attention module is proposed and paired with a multi-scale convolutional model to builda multi-view self-attention convolutional network for multi-channel EEG emotion recognition. First, timeand frequency domain characteristics are extracted from multi-channel EEG signals, and a three-dimensionalfeature matrix is built using spatial mapping connections. Then, a multi-scale convolutional network extractsthe high-level abstract features from the feature matrix, and a multi-view self-attention network strengthensthe features. Finally, use the multilayer perceptron for sentiment

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Zhang, Zhi, Shenghua Zhong, and Yan Liu. "Beyond Mimicking Under-Represented Emotions: Deep Data Augmentation with Emotional Subspace Constraints for EEG-Based Emotion Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 9 (2024): 10252–60. http://dx.doi.org/10.1609/aaai.v38i9.28891.

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Анотація:

In recent years, using Electroencephalography (EEG) to recognize emotions has garnered considerable attention. Despite advancements, limited EEG data restricts its potential. Thus, Generative Adversarial Networks (GANs) are proposed to mimic the observed distributions and generate EEG data. However, for imbalanced datasets, GANs struggle to produce reliable augmentations for under-represented minority emotions by merely mimicking them. Thus, we introduce Emotional Subspace Constrained Generative Adversarial Networks (ESC-GAN) as an alternative to existing frameworks. We first propose the EEG e

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Li, Qi, Yunqing Liu, Yujie Shang, Qiong Zhang, and Fei Yan. "Deep Sparse Autoencoder and Recursive Neural Network for EEG Emotion Recognition." Entropy 24, no. 9 (2022): 1187. http://dx.doi.org/10.3390/e24091187.

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Анотація:

Recently, emotional electroencephalography (EEG) has been of great importance in brain–computer interfaces, and it is more urgent to realize automatic emotion recognition. The EEG signal has the disadvantages of being non-smooth, non-linear, stochastic, and susceptible to background noise. Additionally, EEG signal processing network models have the disadvantages of a large number of parameters and long training time. To address the above issues, a novel model is presented in this paper. Initially, a deep sparse autoencoder network (DSAE) was used to remove redundant information from the EEG si

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Kalashami, Mahsa Pourhosein, Mir Mohsen Pedram, and Hossein Sadr. "EEG Feature Extraction and Data Augmentation in Emotion Recognition." Computational Intelligence and Neuroscience 2022 (March 28, 2022): 1–16. http://dx.doi.org/10.1155/2022/7028517.

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Анотація:

Emotion recognition is a challenging problem in Brain-Computer Interaction (BCI). Electroencephalogram (EEG) gives unique information about brain activities that are created due to emotional stimuli. This is one of the most substantial advantages of brain signals in comparison to facial expression, tone of voice, or speech in emotion recognition tasks. However, the lack of EEG data and high dimensional EEG recordings lead to difficulties in building effective classifiers with high accuracy. In this study, data augmentation and feature extraction techniques are proposed to solve the lack of dat

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Goshvarpour, Atefeh, and Ateke Goshvarpour. "Emotion Recognition Using a Novel Granger Causality Quantifier and Combined Electrodes of EEG." Brain Sciences 13, no. 5 (2023): 759. http://dx.doi.org/10.3390/brainsci13050759.

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Анотація:

Electroencephalogram (EEG) connectivity patterns can reflect neural correlates of emotion. However, the necessity of evaluating bulky data for multi-channel measurements increases the computational cost of the EEG network. To date, several approaches have been presented to pick the optimal cerebral channels, mainly depending on available data. Consequently, the risk of low data stability and reliability has increased by reducing the number of channels. Alternatively, this study suggests an electrode combination approach in which the brain is divided into six areas. After extracting EEG frequen

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Song, Yan, Yiming Yin, and Panfeng Xu. "A Customized ECA-CRNN Model for Emotion Recognition Based on EEG Signals." Electronics 12, no. 13 (2023): 2900. http://dx.doi.org/10.3390/electronics12132900.

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Анотація:

Electroencephalogram (EEG) signals are electrical signals generated by changes in brain potential. As a significant physiological signal, EEG signals have been applied in various fields, including emotion recognition. However, current deep learning methods based on EEG signals for emotion recognition lack consideration of important aspects and comprehensive analysis of feature extraction interactions. In this paper, we propose a novel model named ECA-CRNN for emotion recognition using EEG signals. Our model integrates the efficient channel attention (ECA-Net) module into our modified combinati

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Guo, Yiquan, Bowen Zhang, Xiaomao Fan, Xiaole Shen, and Xiaojiang Peng. "A Comprehensive Interaction in Multiscale Multichannel EEG Signals for Emotion Recognition." Mathematics 12, no. 8 (2024): 1180. http://dx.doi.org/10.3390/math12081180.

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Анотація:

Electroencephalogram (EEG) is the most preferred and credible source for emotion recognition, where long-short range features and a multichannel relationship are crucial for performance because numerous physiological components function at various time scales and on different channels. We propose a cascade scale-aware adaptive graph convolutional network and cross-EEG transformer (SAG-CET) to explore the comprehensive interaction between multiscale and multichannel EEG signals with two novel ideas. First, to model the relationship of multichannel EEG signals and enhance signal representation a

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Hilali, Manal, Abdellah Ezzati, and Said Ben Alla. "CMHFE-DAN: A Transformer-Based Feature Extractor with Domain Adaptation for EEG-Based Emotion Recognition." Information 16, no. 7 (2025): 560. https://doi.org/10.3390/info16070560.

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EEG-based emotion recognition (EEG-ER) through deep learning models has gained more attention in recent years, with more researchers focusing on architecture, feature extraction, and generalisability. This paper presents a novel end-to-end deep learning framework for EEG-ER, combining temporal feature extraction, self-attention mechanisms, and adversarial domain adaptation. The architecture entails a multi-stage 1D CNN for spatiotemporal features from raw EEG signals, followed by a transformer-based attention module for long-range dependencies, and a domain-adversarial neural network (DANN) mo

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Mouazen, Badr, Ayoub Benali, Nouh Taha Chebchoub, El Hassan Abdelwahed, and Giovanni De Marco. "Enhancing EEG-Based Emotion Detection with Hybrid Models: Insights from DEAP Dataset Applications." Sensors 25, no. 6 (2025): 1827. https://doi.org/10.3390/s25061827.

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Анотація:

Emotion detection using electroencephalogram (EEG) signals is a rapidly evolving field with significant applications in mental health diagnostics, affective computing, and human–computer interaction. However, existing approaches often face challenges related to accuracy, interpretability, and real-time feasibility. This study leverages the DEAP dataset to explore and evaluate various machine learning and deep learning techniques for emotion recognition, aiming to address these challenges. To ensure reproducibility, we have made our code publicly available . Extensive experimentation was conduc

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Xia, Yuxiao, and Yinhua Liu. "EEG-Based Emotion Recognition with Consideration of Individual Difference." Sensors 23, no. 18 (2023): 7749. http://dx.doi.org/10.3390/s23187749.

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Анотація:

Electroencephalograms (EEGs) are often used for emotion recognition through a trained EEG-to-emotion models. The training samples are EEG signals recorded while participants receive external induction labeled as various emotions. Individual differences such as emotion degree and time response exist under the same external emotional inductions. These differences can lead to a decrease in the accuracy of emotion classification models in practical applications. The brain-based emotion recognition model proposed in this paper is able to sufficiently consider these individual differences. The propo

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Phan, Tran-Dac-Thinh, Soo-Hyung Kim, Hyung-Jeong Yang, and Guee-Sang Lee. "EEG-Based Emotion Recognition by Convolutional Neural Network with Multi-Scale Kernels." Sensors 21, no. 15 (2021): 5092. http://dx.doi.org/10.3390/s21155092.

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Анотація:

Besides facial or gesture-based emotion recognition, Electroencephalogram (EEG) data have been drawing attention thanks to their capability in countering the effect of deceptive external expressions of humans, like faces or speeches. Emotion recognition based on EEG signals heavily relies on the features and their delineation, which requires the selection of feature categories converted from the raw signals and types of expressions that could display the intrinsic properties of an individual signal or a group of them. Moreover, the correlation or interaction among channels and frequency bands

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Shalu Verma. "Towards Improved Biometric Security: EEG-Based Person Identification Enhanced by Deep Learning and Facial Recognition." Communications on Applied Nonlinear Analysis 32, no. 4s (2024): 168–82. https://doi.org/10.52783/cana.v32.2747.

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Анотація:

Due to their inherent qualities of being secretive, vivid, and unpredictable, electroencephalogram (EEG) signals are considered a valuable tool for security-related identification. However, research on using EEG signals for person identification is still in its early stages. The challenges lie in decoding these signals accurately and implementing effective EEG-based identification methods. In recent years, EEG has been at the forefront of scientific research on User Authentication (UA), leading to innovative experiments that aim to identify individuals based on their unique brain activity in s

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