Robust Spatial Filters on Three-Class Motor Imagery EEG Data Using Independent Component Analysis

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Journal of Biosciences and Medicines

ISSN Print: 2327-5081
ISSN Online: 2327-509X
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"Robust Spatial Filters on Three-Class Motor Imagery EEG Data Using Independent Component Analysis"
written by Bangyan Zhou, Xiaopei Wu, Lei Zhang, Zhao Lv, Xiaojing Guo,
published by Journal of Biosciences and Medicines, Vol.2 No.2, 2014

has been cited by the following article(s):

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[1]	Cross-Subject & Cross-Dataset Subject Transfer in Motor Imagery BCI systems
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[2]	CNN-based framework using spatial dropping for enhanced interpretation of neural activity in motor imagery classification
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[3]	Early Classification of Motor Tasks Using Dynamic Functional Connectivity Graphs from EEG
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[4]	Brain-Computer Interface of Motor Imagery Using ICA and Recurrent Neural Networks
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[5]	Brain Network Analysis by Stable and Unstable EEG Components
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[6]	Comprehensive review on brain–computer interface sensor-based smart home appliances control system
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[7]	A CNN-based Approach for three-class classification of motor imagery EEG data including 'rest state'in hybrid multi-user BCI
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[8]	Multi-class EEG signal classification and acquisition system for brain-computer interface
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[9]	New strategies for pre-processing, feature extraction and classification in BCI systems: Novas estratégias para pré-processamento, extração de atributos e …
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[10]	An Experimental Analysis of Motor Imagery EEG Signals Using Feature Extraction and Classification Methodologies
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[11]	A spatial filtering approach to environmental emotion perception based on electroencephalography
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[12]	An ICA-based spatial filtering approach to saccadic EOG signal recognition
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[13]	Application of Convolutional Neural Networks to Four-Class Motor Imagery Classification Problem.
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[14]	Progress in EEG-Based Brain Robot Interaction Systems
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[15]	Optimised use of independent component analysis for EEG signal processing
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[16]	Motor Imagery Movement and Neurological Disorder Classification using Salient Features of EEG Signal
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[17]	Support vector machines to detect physiological patterns for EEG and EMG-based human–computer interaction: a review
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[18]	Convolutional Neural Networks for Four-Class Motor Imagery Data Classification
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[19]	Application of Convolutional Neural Networks to Four-Class Motor Imagery Classification Problem
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[20]	НЕЙРОТЕХНОЛОГИИ: НЕЙРО-БОС И ИНТЕРФЕЙС «МОЗГ–КОМПЬЮТЕР»
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[21]	CLASSIFICATION METHODS FOR MOTOR IMAGERY BASED BRAIN COMPUTER INTERFACES
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[22]	Multiclass motor imagery classification for BCI application
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[23]	Motor-imagery EEG signal classification using position matching and vector quantisation
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[24]	ThinICA-CSP algorithm for discrimination of multiclass motor imagery movements
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[25]	基于独立分量分析的扫视信号样本优化算法
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[26]	Motor Hareket Hayali Tabanlı Beyin Bilgisayar Arayüzleri İçin Sınıflandırma Metotları
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[27]	Determining AR order for BCI based on motor imagery
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[1]	Cross-Subject & Cross-Dataset Subject Transfer in Motor Imagery BCI systems 2022 International Joint Conference on Neural Networks (IJCNN), 2022 DOI:10.1109/IJCNN55064.2022.9891904

[2]	Brain Network Analysis by Stable and Unstable EEG Components IEEE Journal of Biomedical and Health Informatics, 2021 DOI:10.1109/JBHI.2020.3015471

[3]	Early classification of motor tasks using dynamic functional connectivity graphs from EEG Journal of Neural Engineering, 2021 DOI:10.1088/1741-2552/abce70

[4]	Brain-Computer Interface of Motor Imagery Using ICA and Recurrent Neural Networks 2020 3rd International Conference on Computer and Informatics Engineering (IC2IE), 2020 DOI:10.1109/IC2IE50715.2020.9274681

[5]	A CNN-based Approach for three-class classification of motor imagery EEG data including ‘rest state’ in hybrid multi-user BCI 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2019 DOI:10.1109/BIBM47256.2019.8983380

[6]	An Experimental Analysis of Motor Imagery EEG Signals Using Feature Extraction and Classification Methodologies 2018 International Conference on Computing, Power and Communication Technologies (GUCON), 2018 DOI:10.1109/GUCON.2018.8675032

[7]	Support vector machines to detect physiological patterns for EEG and EMG-based human–computer interaction: a review Journal of Neural Engineering, 2017 DOI:10.1088/1741-2552/14/1/011001

[8]	Progress in EEG-Based Brain Robot Interaction Systems Computational Intelligence and Neuroscience, 2017 DOI:10.1155/2017/1742862

[9]	Multiclass motor imagery classification for BCI application 2016 International Workshop on Computational Intelligence (IWCI), 2016 DOI:10.1109/IWCI.2016.7860335

[10]	Determining AR order for BCI based on motor imagery 2015 8th International Conference on Biomedical Engineering and Informatics (BMEI), 2015 DOI:10.1109/BMEI.2015.7401495

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