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A Deep Transfer Convolutional Neural Network Framework for EEG Signal Classification

Gaowei Xu, Xiaoang Shen, Sirui Chen, Yongshuo Zong, Canyang Zhang, Hongyang yue, Min Liu, Fei Chena, Wenliang Che
2019 IEEE Access  
To cope with these two challenges, a deep transfer convolutional neural network (CNN) framework based on VGG-16 is proposed for EEG signal classification.  ...  The parameters of the pre-trained VGG-16 CNN model are directly transferred to the target CNN model used for MI EEG signal classification.  ...  Then, a deep transfer CNN framework is proposed for EEG signal classification, which takes the obtained images as the input.  ... 
doi:10.1109/access.2019.2930958 fatcat:dfvsm62wyrdlhnf5ugc25ryxfe

Deep Learning Algorithms in EEG Signal Decoding Application: A Review

Ramesh Babu Vallabhaneni, Pankaj Sharma, Vinit Kumar, Vyom Kulshreshtha, Koya Jeevan Reddy, S Selva Kumar, V Sandeep Kumar, Surendra Kumar Bitra
2021 IEEE Access  
Firstly, the basic principles of deep learning algorithms used in EEG decoding is briefly described, including convolutional neural network, deep belief network, autoencoder and recurrent neural network  ...  Especially, there has been an increasing focus on the use of deep learning algorithms for decoding physiological or pathological status of the brain from electroencephalographic (EEG).  ...  A. CONVOLUTIONAL NEURAL NETWORK The artificial neural network model i.e., CNN is very effective for image classification.  ... 
doi:10.1109/access.2021.3105917 fatcat:rsl44hjcu5cw5mxy5xye5owzuy

Guest Editors' Introduction: Special issue on deep learning with applications to visual representation and analysis

Lei Wang, Ce Zhu, Jieping Ye, Juergen Gall
2016 Signal processing. Image communication  
To take the characteristics of EEG data into account, it develops a novel channel-wise convolutional neural network to learn feature representation for classification.  ...  EEG signals.  ... 
doi:10.1016/j.image.2016.09.003 fatcat:ufz6tbhkwzhapjysq564cm5e54

Deep Transfer Learning for EEG-based Brain Computer Interface [article]

Chuanqi Tan, Fuchun Sun, Wenchang Zhang
2018 arXiv   pre-print
Second, we design a deep transfer learning framework which is suitable for transferring knowledge by joint training, which contains a adversarial network and a special loss function.  ...  Herein, we propose a novel deep transfer learning approach to solve these two problems.  ...  [5] used a convolutional neural network (CNN) to decode P300 patterns.  ... 
arXiv:1808.01752v1 fatcat:vlcxkl3wyjggpghoxgeau7rqbe

Guest Editorial: Advanced Machine-Learning Methods for Brain-Machine Interfacing or Brain-Computer Interfacing

Kaijian Xia, Yizhang Jiang, Yudong Zhang, Wen Si
2021 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
classification model (called HD-SRC) for EEG signal detection.  ...  Based on the framework of neural network, HD-SRC learns the hierarchical nonlinear transformation and maps the signal data into the nonlinear transformed space.  ...  classification model (called HD-SRC) for EEG signal detection.  ... 
doi:10.1109/tcbb.2021.3078145 fatcat:hojaokclpjhgpcqob5jbwrlktm

EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications [article]

Xiaotong Gu, Zehong Cao, Alireza Jolfaei, Peng Xu, Dongrui Wu, Tzyy-Ping Jung, Chin-Teng Lin
2020 arXiv   pre-print
Then, we present advanced signal sensing and enhancement technologies to collect and clean EEG signals, respectively.  ...  In this study, we survey the recent literature of EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensated for the gaps in the systematic summary of the  ...  ACKNOWLEDGMENT Credit authors for icons made from  ... 
arXiv:2001.11337v1 fatcat:cmurfjykjja3rdifr7e7cqq3wy

Instance Transfer Subject-Dependent Strategy for Motor Imagery Signal Classification Using Deep Convolutional Neural Networks

Kai Zhang, Guanghua Xu, Longtin Chen, Peiyuan Tian, ChengCheng Han, Sicong Zhang, Nan Duan
2020 Computational and Mathematical Methods in Medicine  
especially for a deep neural network (DNN) classification model.  ...  Herein, we propose an instance transfer subject-independent (ITSD) framework combined with a convolutional neural network (CNN) to improve the classification accuracy of the model during motor imagery  ...  BCI competition IV dataset 2b. design a decoding model for EEGs based on transfer learning combined with the deep neural network.  ... 
doi:10.1155/2020/1683013 pmid:32908576 pmcid:PMC7474754 fatcat:uijbeddeazczpns73wknxul73q

A Parallel Multiscale Filter Bank Convolutional Neural Networks for Motor Imagery EEG Classification

Hao Wu, Yi Niu, Fu Li, Yuchen Li, Boxun Fu, Guangming Shi, Minghao Dong
2019 Frontiers in Neuroscience  
In this study, we propose a parallel multiscale filter bank convolutional neural network (MSFBCNN) for MI classification.  ...  To enhance the transfer learning ability, we propose a network initialization and fine-tuning strategy to train an individual model for inter-subject classification on small datasets.  ...  As follows from the literature, designing a feasible end-to-end deep neural architecture for MI-EEG classification remains a challenge.  ... 
doi:10.3389/fnins.2019.01275 pmid:31849587 pmcid:PMC6901997 fatcat:yot5qoufcbgh3mahxiswvvccc4

Deep Convolutional Neural Network-Based Epileptic Electroencephalogram (EEG) Signal Classification

Yunyuan Gao, Bo Gao, Qiang Chen, Jia Liu, Yingchun Zhang
2020 Frontiers in Neurology  
This methodology first transforms epileptic EEG signals to power spectrum density energy diagrams (PSDEDs), then applies deep convolutional neural networks (DCNNs) and transfer learning to automatically  ...  A new deep learning-based classification methodology, namely epileptic EEG signal classification (EESC), is proposed in this paper.  ...  Epileptic EEG Signal Classification Here, epileptic EEG signal classification (EESC) is used for classifying four different epileptic states by using deep convolutional neural networks (DCNNs) and transfer  ... 
doi:10.3389/fneur.2020.00375 pmid:32528398 pmcid:PMC7257380 fatcat:o254cir4vzb6ncmi5bxjwiyfze

Two-Level Domain Adaptation Neural Network for EEG-Based Emotion Recognition

Guangcheng Bao, Ning Zhuang, Li Tong, Bin Yan, Jun Shu, Linyuan Wang, Ying Zeng, Zhichong Shen
2021 Frontiers in Human Neuroscience  
In this paper, we propose a two-level domain adaptation neural network (TDANN) to construct a transfer model for EEG-based emotion recognition.  ...  Specifically, deep features from the topological graph, which preserve topological information from EEG signals, are extracted using a deep neural network.  ...  LT is mainly responsible for research design and data analysis. JS is mainly responsible for data collection and production of charts.  ... 
doi:10.3389/fnhum.2020.605246 pmid:33551775 pmcid:PMC7854906 fatcat:wgjb32vvmjcedgrxgbuxw4fgga

Hierarchical Deep Feature Learning For Decoding Imagined Speech From EEG [article]

Pramit Saha, Sidney Fels
2019 arXiv   pre-print
We propose a mixed deep neural network strategy, incorporating parallel combination of Convolutional (CNN) and Recurrent Neural Networks (RNN), cascaded with deep autoencoders and fully connected layers  ...  Our approach demonstrates the promise of a mixed DNN approach for complex spatial-temporal classification problems.  ...  The network is trained hierarchically on a channel covariance matrix for categorizing respective EEG signals to the imagined speech classes.  ... 
arXiv:1904.04352v1 fatcat:lb4n5odqfbcopjloaidjavrera

Multichannel Sleep Stage Classification and Transfer Learning using Convolutional Neural Networks

Fernando Andreotti, Huy Phan, Navin Cooray, Christine Lo, Michele T. M. Hu, Maarten De Vos
2018 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)  
Convolutional neural networks (CNN) provide an interesting framework to automated classification of sleep based on these raw waveforms.  ...  Deep learning approaches require a lot of data which is scarce for less prevalent diseases.  ...  CONCLUSION In this work, we apply several deep convolutional neural networks to the task of automated sleep staging.  ... 
doi:10.1109/embc.2018.8512214 pmid:30440365 fatcat:zldkkdpaezhutbjjwwm56dtsyy

An efficient multi-scale CNN model with intrinsic feature integration for motor imagery EEG subject classification in brain-machine interfaces [article]

Arunabha Mohan Roy
2022 bioRxiv   pre-print
canonical frequency bands of EEG signals from multiple scales for MI-BCI classification.  ...  The current framework can provide a stimulus for designing efficient and robust real-time human-robot interaction.  ...  Along similar line, a deep transfer CNN framework based on the VGG-16 CNN model pre-trained on the ImageNet and a target CNN model for MI EEG signal classification has been proposed in [29] .  ... 
doi:10.1101/2022.01.05.475058 fatcat:pvuoktelurcphgew7y5vsdzpze

EEG-based image classification via a region-level stacked bi-directional deep learning framework

Ahmed Fares, Sheng-hua Zhong, Jianmin Jiang
2019 BMC Medical Informatics and Decision Making  
As a physiological signal, EEG data cannot be subjectively changed or hidden.  ...  While such an enormous advance has attracted wide range of interests among relevant research communities, EEG-based classification of brain activities evoked by images still demands efforts for further  ...  Science Foundation of China , the Shenzhen Emerging Industries of the Strategic Basic Research Project under Grant, the Shenzhen high-level overseas talents program, the National Engineering Laboratory for  ... 
doi:10.1186/s12911-019-0967-9 pmid:31856818 pmcid:PMC6921386 fatcat:5msd3nlzdzevfi4c25yl6k7c7q

Hierarchical Deep Feature Learning for Decoding Imagined Speech from EEG

Pramit Saha, Sidney Fels
We propose a mixed deep neural network strategy, incorporating parallel combination of Convolutional (CNN) and Recurrent Neural Networks (RNN), cascaded with deep autoencoders and fully connected layers  ...  Our approach demonstrates the promise of a mixed DNN approach for complex spatialtemporal classification problems.  ...  The network is trained hierarchically on a channel covariance matrix for categorizing respective EEG signals to the imagined speech classes.  ... 
doi:10.1609/aaai.v33i01.330110019 fatcat:ltpfcgtgsfgflmphlunheozaaa
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