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Guest Editorial Multimodal Modeling and Analysis Informed by Brain Imaging—Part I

2015 IEEE Transactions on Autonomous Mental Development  
In their paper, "Investigating Critical Frequency Bands and Channels for EEG-based Emotion Recognition with Deep Neural Networks," Zheng and Lu [1] employ deep belief networks (DBN) to identify the critical  ...  They also report significant difference in the physiological signals between tone-mapped HDR and LDR videos in the classification under both subject-dependent and subject-independent scenarios.  ... 
doi:10.1109/tamd.2015.2495698 fatcat:j32qqqtiwjhktacprjgrf5prt4

Feedback Artificial Shuffled Shepherd Optimization-Based Deep Maxout Network for Human Emotion Recognition Using EEG Signals

K. S. Bhanumathi, D. Jayadevappa, Satish Tunga, Fei Hu
2022 International Journal of Telemedicine and Applications  
EEG signals.  ...  Hence, an effective human recognition approach is designed using the proposed feedback artificial shuffled shepherd optimization- (FASSO-) based deep maxout network (DMN) for recognizing emotions using  ...  [15] introduced a graph convolutional neural networks (ECLGCNN) and LSTM for recognizing the emotions using EEG signals.  ... 
doi:10.1155/2022/3749413 pmid:35282409 pmcid:PMC8904914 fatcat:diqw4ra2djbe5h3wjrjuvks5vi

CR-GCN: Channel-Relationships-Based Graph Convolutional Network for EEG Emotion Recognition

Jingjing Jia, Bofeng Zhang, Hehe Lv, Zhikang Xu, Shengxiang Hu, Haiyan Li
2022 Brain Sciences  
EEG-based methods have been widely used for emotion recognition recently.  ...  However, most current methods for EEG-based emotion recognition do not fully exploit the relationship of EEG channels, which affects the precision of emotion recognition.  ...  [16] used a convolutional neural network (CNN) to extract features of different EEG channels to realize emotion recognition.  ... 
doi:10.3390/brainsci12080987 pmid:35892427 pmcid:PMC9394289 fatcat:wdkpassexjekfbtavbebcfwz3y

Multisensory integration of dynamic emotional faces and voices: method for simultaneous EEG-fMRI measurements

Patrick D. Schelenz, Martin Klasen, Barbara Reese, Christina Regenbogen, Dhana Wolf, hb Yutaka Kato, Klaus Mathiak
2013 Frontiers in Human Neuroscience  
Our results suggest a valid methodological approach to investigate complex stimuli using the present source localization driven method for EEG-fMRI.  ...  In this pilot study, feasibility and sensitivity of source localization-driven analysis for EEG-fMRI was tested using a multisensory integration paradigm.  ...  They reported reduced workload on a fronto-parietal attention network for emotionally CON multisensory stimuli.  ... 
doi:10.3389/fnhum.2013.00729 pmid:24294195 pmcid:PMC3827626 fatcat:iceu22jw65akpjm3762quma6s4

A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances [article]

Yan Wang, Wei Song, Wei Tao, Antonio Liotta, Dawei Yang, Xinlei Li, Shuyong Gao, Yixuan Sun, Weifeng Ge, Wei Zhang, Wenqiang Zhang
2022 arXiv   pre-print
., textual, audio, and visual data) and physiological signals (e.g., EEG and ECG signals). Physical-based affect recognition caters to more researchers due to multiple public databases.  ...  Thus, the fusion of physical information and physiological signals can provide useful features of emotional states and lead to higher accuracy.  ...  Zheng [330] [331] [332] from Southeast University, China, has proposed various EEG-based emotion recognition networks such as bi-hemispheres domain adversarial neural network (BiDANN) [330] , instance-adaptive  ... 
arXiv:2203.06935v3 fatcat:h4t3omkzjvcejn2kpvxns7n2qe

Using Facial Micro-Expressions in Combination With EEG and Physiological Signals for Emotion Recognition

Nastaran Saffaryazdi, Syed Talal Wasim, Kuldeep Dileep, Alireza Farrokhi Nia, Suranga Nanayakkara, Elizabeth Broadbent, Mark Billinghurst
2022 Frontiers in Psychology  
We also discuss future directions for using facial micro-expressions and physiological signals in emotion recognition.  ...  We then evaluate our model using the DEAP dataset and our own dataset based on a subject-independent approach.  ...  Lee et al. (2019) used a one-dimensional convolutional neural network (1D CNN) to extract deep features of PPG signals and classify emotional states.  ... 
doi:10.3389/fpsyg.2022.864047 pmid:35837650 pmcid:PMC9275379 fatcat:g6wqbqn6ejh4dp7ks6rfnpx4eu

Advertising Liking Recognition Technique Applied to Neuromarketing by Using Low-Cost EEG Headset [chapter]

Luis Miguel Soria Morillo, Juan Antonio Alvarez García, Luis Gonzalez-Abril, J. A. Ortega Ramirez
2015 Lecture Notes in Computer Science  
Using low cost electroencephalography (EEG), brain regions used during the presentation have been studied.  ...  This techniques allows to reach more than 82% of accuracy, which is an excellent result taking into account the kind of low-cost EEG sensors used.  ...  The testing and validation process employed in this case has been the same to the previous setting using ANN (Artificial Neural Networks) [Hagan et al.(1996)Hagan, Demuth, Beale, et al.] , so the results  ... 
doi:10.1007/978-3-319-16480-9_68 fatcat:qx2koppspbedfhagtzkpwqgdle

Representation Learning and Pattern Recognition in Cognitive Biometrics: A Survey

Min Wang, Xuefei Yin, Yanming Zhu, Jiankun Hu
2022 Sensors  
Recent research has demonstrated great potential for using cognitive biometrics in versatile applications, including biometric recognition and cognitive and emotional state recognition.  ...  A taxonomy is designed to structure the corresponding knowledge and guide the survey from signal acquisition and pre-processing to representation learning and pattern recognition.  ...  [187] proposed an LSTM-based method for emotion recognition using multichannel EEG signals. Zhang et al.  ... 
doi:10.3390/s22145111 pmid:35890799 pmcid:PMC9320620 fatcat:7cniceltrbfkjk6hxfqij5yegm

Spatiotemporal Emotion Recognition using Deep CNN Based on EEG during Music Listening [article]

Panayu Keelawat, Nattapong Thammasan, Masayuki Numao, Boonserm Kijsirikul
2019 arXiv   pre-print
Emotion recognition based on EEG has become an active research area. As one of the machine learning models, CNN has been utilized to solve diverse problems including issues in this domain.  ...  Our investigation was conducted in subject-independent fashion.  ...  EEG signals were collected from twelve subjects and signal processing techniques were employed to reduce effects from unrelated artifacts.  ... 
arXiv:1910.09719v1 fatcat:2brdup3xjncclfcinviw5faryi

Past, Present, and Future of EEG-Based BCI Applications

Kaido Värbu, Naveed Muhammad, Yar Muhammad
2022 Sensors  
In this review, the equipment used for gathering EEG data and signal processing methods have also been reviewed.  ...  An electroencephalography (EEG)-based brain–computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG.  ...  Kalafatovich et al. 2020 Decoding Visual Recognition of Objects from EEG Signals based on Attention-Driven Convolutional Neural Network Kang et al. 2020 EEG-Based Prediction of Successful Memory Formation  ... 
doi:10.3390/s22093331 pmid:35591021 pmcid:PMC9101004 fatcat:gn6bt4uqavenzbu3nkt32de42m

Listen to your Mind's (He)Art: A System for Affective Music Generation via Brain-Computer Interface

Marco Tiraboschi, Federico Avanzini, Giuseppe Boccignone
2021 Zenodo  
We present an approach to the problem of real-time generation of music, driven by the affective state of the user, estimated from their electroencephalogram (EEG).  ...  It manages communication with the EEG device and computes the relevant features.  ...  It is a dataset collecting EEG and physiological signals recorded from 32 subjects over 40 trials per subject. The authors also presented some approaches to the emotion recognition task.  ... 
doi:10.5281/zenodo.5044984 fatcat:xxnivv5evfchxaeyvstyygyilq

Lightweight Building of an Electroencephalogram-Based Emotion Detection System

Abeer Al-Nafjan, Khulud Alharthi, Heba Kurdi
2020 Brain Sciences  
The results showed that by using a NeuCube-based spiking neural network, we could detect the valence emotion level using only 60 EEG samples with 84.62% accuracy, which is a comparable accuracy to that  ...  In this study, we investigated the feasibility of using a spiking neural network to build an emotion detection system from a smaller version of the DEAP dataset with no involvement of feature extraction  ...  In [35] , the researchers proposed an EEG-based emotion recognition method based on empirical mode decomposition (EMD), which is a data-driven signal processing analysis technique.  ... 
doi:10.3390/brainsci10110781 pmid:33114646 fatcat:wcyrlfgnc5gfdavjfyftapzpnm

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

Mamunur Rashid, Norizam Sulaiman, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Ahmad Fakhri Ab. Nasir, Bifta Sama Bari, Sabira Khatun
2020 Frontiers in Neurorobotics  
First, a brief overview of electroencephalogram (EEG)-based BCI systems is given.  ...  It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention.  ...  In emotion recognition using an EEG signal, the fractal dimension of raw signals has been implemented to extract the feature by using the Higuchi technique (Anh et al., 2012; Kaur et al., 2018) .  ... 
doi:10.3389/fnbot.2020.00025 pmid:32581758 pmcid:PMC7283463 fatcat:jhpwp2b3hffz5mazb7y6oj3saq

DE-CNN: An Improved Identity Recognition Algorithm Based on the Emotional Electroencephalography

Yingdong Wang, Qingfeng Wu, Chen Wang, Qunsheng Ruan
2020 Computational and Mathematical Methods in Medicine  
In the present study, a novel EEG-based identification system with different entropy and a continuous convolution neural network (CNN) classifier is proposed.  ...  In the past few decades, identification recognition based on electroencephalography (EEG) has received extensive attention to resolve the security problems of conventional biometric systems.  ...  [16] used the biggest motion imagination EEG dataset. ey applied the1Dconvolutional long short-term memory neural networks to identify 109 subjects, where the best result was about 0.0041in terms of  ... 
doi:10.1155/2020/7574531 pmid:32849910 pmcid:PMC7439782 fatcat:7xemuoibjvbfdh7fp4b65vtebi

Development and Progress in Sensors and Technologies for Human Emotion Recognition

Shantanu Pal, Subhas Mukhopadhyay, Nagender Suryadevara
2021 Sensors  
We review the state-of-the-art sensors used for human emotion recognition and different types of activity monitoring.  ...  The increased use of online platforms for communication signifies the need to build efficient and more interactive human emotion recognition systems.  ...  Therefore, EEG-based emotion recognition models are, in general, subject dependent. Several proposals study the need for subject independent emotion recognition based on EEG signals.  ... 
doi:10.3390/s21165554 pmid:34451002 pmcid:PMC8402266 fatcat:z43zfssx3zatfg736tejes3ytq
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