Results 11 to 20 of about 3,887,920 (279)
Schizophrenia EEG Signal Classification Based on Swarm Intelligence Computing. [PDF]
Comput Intell Neurosci, 2020 One of the serious mental disorders where people interpret reality in an abnormal state is schizophrenia. A combination of extremely disordered thinking, delusion, and hallucination is caused due to schizophrenia, and the daily functions of a person are ...
Prabhakar SK, Rajaguru H, Kim SH.
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Multiclass EEG signal classification utilizing Rényi min-entropy-based feature selection from wavelet packet transformation. [PDF]
Brain Inform, 2020 This paper proposes a novel feature selection method utilizing Rényi min-entropy-based algorithm for achieving a highly efficient brain–computer interface (BCI). Usually, wavelet packet transformation (WPT) is extensively used for feature extraction from
Rahman MA, Khanam F, Ahmad M, Uddin MS.
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A Dynamic Multi-Scale Network for EEG Signal Classification. [PDF]
Front Neurosci, 2020 Accurate and automatic classification of the speech imagery electroencephalography (EEG) signals from a Brain-Computer Interface (BCI) system is highly demanded in clinical diagnosis.
Zhang G +6 more
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Pattern Recognition Letters, 2021 With the promotion of brain-computer interface technology, it is possible to study brain control system through EEG signals in recent years. In order to solve the problem of EEG signal classification effectively, a motor imagery classification algorithm ...
Keyang Cheng, Xianjia Meng
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Auto-Weighted Multi-View Discriminative Metric Learning Method With Fisher Discriminative and Global Structure Constraints for Epilepsy EEG Signal Classification [PDF]
Frontiers in Neuroscience, 2020 Metric learning is a class of efficient algorithms for EEG signal classification problem. Usually, metric learning method deals with EEG signals in the single view space. To exploit the diversity and complementariness of different feature representations,
Jing Xue, Xiaoqing Gu, Tongguang Ni
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Deep Learning Approach EEG Signal Classification
JOIV: International Journal on Informatics VisualizationThe introduction of deep learning technology has greatly benefited the neuroscience field by improving the electroencephalogram (EEG) signal analysis. These technologies have greatly improved the understanding of complex brain activity by interpreting ...
Kai Liang Lew, Kok Swee Sim, Zehong Ting
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Epilepsy EEG Signal Classification Algorithm Based on Improved RBF. [PDF]
Front Neurosci, 2020 Epilepsy is a chronic recurrent transient brain dysfunction syndrome. It is characterized by recurrent epilepsy caused by abnormal discharge of brain neurons. Epilepsy is one of the common diseases in nervous system.
Zhou D, Li X.
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SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2022 In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for epileptic electroencephalogram (EEG) signal classification. The goal is to develop a lightweighted deep learning model while retaining a high level of classification ...
Jialin Wang +4 more
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Радіоелектронні і комп'ютерні системи, 2023 The article’s subject matter is to classify Electroencephalography (EEG) signals in Autism Spectrum Disorder (ASD) sufferers. The goal is to develop a classification model using Machine Learning (ML) algorithms that are often implemented in Brain ...
Melinda Melinda +5 more
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EEG Decoding Based on Wavelet Transform and Common Space Pattern [PDF]
Zhengzhou Daxue xuebao. Gongxue ban, 2022 Aiming at the problem of fewer tasks and low accuracy of recognition for motion imagination electroencephalogram(EEG)signals, in this paper a common space pattern(CSP)method based on wavelet packet decomposition(WPD)was proposed to extract the features
QU Silin +4 more
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