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Accelerating 3D Convolutional Neural Network with Channel Bottleneck Module for EEG-Based Emotion Recognition
2022
Sensors
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Here, we propose a novel 3D convolutional neural network with a channel bottleneck module (CNN-BN) model for EEG-based emotion recognition, with the aim of accelerating the CNN computation without a significant ...
Although deep learning models for EEG-based emotion recognition can deliver superior accuracy, it comes at the cost of high computational complexity. ...
Before applying the ToC, a discrete wavelet transform (DWT) is used to decompose the EEG signal into five frequency sub-bands and a particle swarm optimization algorithm is used to optimize the feature ...
doi:10.3390/s22186813
pmid:36146160
pmcid:PMC9500982
fatcat:6nw2etjmprb7pkvtep5xqhwj4a
Person-Independent Emotion and Gender Prediction (EGP) System Using EEG Signals
2022
˜The œinternational Arab journal of information technology
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For emotion prediction, the highest average accuracy is 97.07\%, 93.27% and 91.72\% for three, four and six emotions with Convolutional Neural Network (CNN) classifier, respectively. ...
While, for gender prediction, experiments are tested related to neutral, amusement, happy, sad, and the mix of all these emotions, the highest average accuracy is obtained with CNN classifier in all emotion ...
The ANOVA, Binary Gravitation Search Algorithm (BGSA) and Binary Particle Swarm Optimization (BPSO) were implemented on the extracted features to get the optimal channels for gender prediction. ...
doi:10.34028/iajit/19/4/7
fatcat:3wmm2v4zirftpf5lkwdfoew2mi
Deep Learning in EEG: Advance of the Last Ten-Year Critical Period
2021
IEEE Transactions on Cognitive and Developmental Systems
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Subsequently, the applications of deep learning in EEG are reviewed by categorizing them into groups such as brain-computer interface, disease detection, and emotion recognition. ...
We hope that this paper could serve as a summary of past work for deep learning in EEG and the beginning of further developments and achievements of EEG studies based on deep learning. ...
Gao et al. utilized gradient priority particle swarm optimization to optimize parameters of a CNN model [105] .
D. ...
doi:10.1109/tcds.2021.3079712
fatcat:5rck4hvysfhe5o2tfjywytr5o4
A Review of Emotion Recognition Using Physiological Signals
2018
Sensors
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, features, classifiers, and the whole framework for emotion recognition based on the physiological signals. ...
In this paper, we present a comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods, the published emotional physiological datasets ...
RSP Researchers of [72] used particle swarm optimization (PSO) of synergetic neural classifier for emotion recognition with signals of EMG, ECG, SC, RSP. ...
doi:10.3390/s18072074
pmid:29958457
pmcid:PMC6069143
fatcat:einxw5uc7fdxherrwenpggdruq
A Detailed Analysis & Parametric Comparison Of Eeg Processing Models
2022
Journal of Pharmaceutical Negative Results
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Thus, it is difficult for researchers to identify optimal EEG processing models for their clinical use cases. ...
Thus, this text will allow readers to identify optimally performing models for different EEG processing scenarios. ...
The Bat Algorithm (BA), Cuckoo Search Optimization (CSO), and Particle Swarm Optimization are used in the first technique, which combines a sparse autoencoder (SAE) with a swarm-based deep learning approach ...
doi:10.47750/pnr.2022.13.s02.44
fatcat:ldltompy2vd3zopgs2zgljhagq
Table of Content
2020
2020 28th Iranian Conference on Electrical Engineering (ICEE)
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wolf optimization ............ 1584A Multi-Label Feature Selection Based on Mutual Information and Ant Colony Optimization ............ 1589 Control of MIMO nonlinear discrete-time systems with input ...
Convolutional Recurrent Network for Load Forecasting ............................................ 885 Functional classification of neurons in mouse hippocampus based on spike waveforms in extracellular ...
doi:10.1109/icee50131.2020.9260902
fatcat:7gs43h5sqraabcu35jsrax4cqu
Can We Ditch Feature Engineering? End-to-End Deep Learning for Affect Recognition from Physiological Sensor Data
2020
Sensors
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Ten end-to-end DL architectures are compared on four different datasets with diverse raw physiological signals used for affect recognition, including emotional and stress states. ...
Additionally, the results showed that the CNN-based architectures might be more suitable than LSTM-based architectures for affect recognition from physiological sensors. ...
Acknowledgments: We would like to thank Joanna Komoszyńska for providing the implementation of CNN-LSTM and external insight into research. ...
doi:10.3390/s20226535
pmid:33207564
pmcid:PMC7697590
fatcat:nfyc6xx67rhmfanpcfms3thnta
Applications of Deep Learning and Reinforcement Learning to Biological Data
2018
IEEE Transactions on Neural Networks and Learning Systems
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Rapid advances of hardware-based technologies during the past decades have opened up new possibilities for Life scientists to gather multimodal data in various application domains (e.g., Omics, Bioimaging ...
, Medical Imaging, and [Brain/Body]-Machine Interfaces), thus generating novel opportunities for development of dedicated data intensive machine learning techniques. ...
Kamal Abu-Hassan for useful discussions during the early stage of the work. This work was supported by the ACSLab (www.acslab.info). ...
doi:10.1109/tnnls.2018.2790388
pmid:29771663
fatcat:6r63zihrfvea7cto4ei3mlvqtu
Analyzing the Role of Emotional Intelligence on the Performance of Small and Medium Enterprises (SMEs) Using AI-Based Convolutional Neural Networks (CNNs)
2022
Security and Communication Networks
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Then the specialized features are retrieved from the body of human gestures using the AdaDelta bacteria foraging optimization algorithm, and the selected features are fed to a supervised Kernel Boosting ...
Human emotion detection is necessary for social interaction and plays an important role in our daily lives. Artificial intelligence research is rising, focusing on automated emotion detection. ...
selected using the binary particle swarm optimization technique. ...
doi:10.1155/2022/7951676
fatcat:muaikcxg6bghhk73ctmmkyp2a4
Advances in Multimodal Emotion Recognition Based on Brain–Computer Interfaces
2020
Brain Sciences
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Finally, we identify several important issues and research directions for multimodal emotion recognition based on BCI. ...
With the continuous development of portable noninvasive human sensor technologies such as brain–computer interfaces (BCI), multimodal emotion recognition has attracted increasing attention in the area ...
From the aspect of emotion recognition based on various hybrid neurophysiology modalities, a method of emotion recognition of multimodal physiological signals based on a convolutional recurrent neural ...
doi:10.3390/brainsci10100687
pmid:33003397
pmcid:PMC7600724
fatcat:juzx77asgrh2zpl3s2jvw6tdcq
Eye State Identification Utilizing EEG Signals: A Combined Method Using Self-Organizing Map and Deep Belief Network
2022
Scientific Programming
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The results on a dataset with 14980 instances and 15 attributes representing the values of the electrodes demonstrated that the method is efficient for EEG analysis. ...
There have been many methods for EEG analysis using supervised and unsupervised machine learning techniques. ...
Furthermore, Particle Swarm Optimization (PSO) [31] was utilized for optimizing the Mean Square Error (MSE) of the trained network and they achieved 99% accuracy rate in their work. e authors in [32 ...
doi:10.1155/2022/4439189
fatcat:3hvwxfryzzaftjz4ap6zip7u2q
Past, Present, and Future of EEG-Based BCI Applications
2022
Sensors
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EEG-based BCI applications have initially been developed for medical purposes, with the aim of facilitating the return of patients to normal life. ...
In this review, 202 publications were selected based on specific eligibility criteria. ...
Accuracy-cost trade-off Li et al. 2020 Enhancing BCI-Based Emotion Recognition Using an Improved Particle Swarm Optimization for Feature Selection Liang et al. 2020 EEG-Based EMG Estimation of Shoulder ...
doi:10.3390/s22093331
pmid:35591021
pmcid:PMC9101004
fatcat:gn6bt4uqavenzbu3nkt32de42m
Unit commitment considering multiple charging and discharging scenarios of plug-in electric vehicles
2015
2015 International Joint Conference on Neural Networks (IJCNN)
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Ganggang Kong
P362 Effectiveness of Random Search in SVM hyper-parameter tuning [#15557]
P363 Face Expression Recognition with a 2-Channel Convolutional Neural Network [#15380]
Dennis Hamester, ...
Changseok Bae
5:20PM A Probability-Dynamic Particle Swarm Optimization for Object Tracking [#15348]
Rohitash Chandra and Gary Wong
6:00PM Design Static Var Compensator Controller Using Artificial ...
doi:10.1109/ijcnn.2015.7280446
dblp:conf/ijcnn/YangLNF15
fatcat:6xlakikcfzfyhhm2spooe2j7ra
Neural Decoding of EEG Signals with Machine Learning: A Systematic Review
2021
Brain Sciences
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A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. ...
This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. ...
Douglas for providing valuable comments and suggestions. ...
doi:10.3390/brainsci11111525
pmid:34827524
pmcid:PMC8615531
fatcat:4ia7yrcptvgqhla7ccozb46xia
A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface
2021
Sensors
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This paper provides a comprehensive review of the electroencephalogram (EEG) based MI-BCI system. ...
We discuss recent developments and critical algorithmic issues in MI-based BCI for commercial deployment. ...
This can be achieved by the particle swarm optimization based learning strategy to find optimal parameters for spiking neural model (SNM) (deep learning model) [126] . ...
doi:10.3390/s21062173
pmid:33804611
pmcid:PMC8003721
fatcat:xgqftpxyajfgtny4mml77k5kfy
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