Channel space weighted fusion-oriented feature pyramid network for motor imagery EEG signal recognition [PDF]
EAI Endorsed Transactions on Scalable Information Systems, 2022 In order to solve the problems of weak generalization ability and low classification accuracy in motor imagery EEG signal classification, this paper proposes a channel space weighted fusion-oriented feature pyramid network for motor imagery
Wenhao Yang
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Two Different Approaches of Feature Extraction for Classifying the EEG Signals [PDF]
, 2011 The electroencephalograph (EEG) signal is one of the most widely used signals in the biomedicine field due to its rich information about human tasks.
Caraça-Valente Hernández, Juan Pedro +12 more
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IEEE Journal of Translational Engineering in Health and Medicine, 2021 Background: Diagnosing epileptic seizures using electroencephalogram (EEG) in combination with deep learning computational methods has received much attention in recent years.
Md. Rashed-Al-Mahfuz +5 more
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Artifact reduction in multichannel pervasive EEG using hybrid WPT-ICA and WPT-EMD signal decomposition techniques [PDF]
, 2014 In order to reduce the muscle artifacts in multi-channel pervasive Electroencephalogram (EEG) signals, we here propose and compare two hybrid algorithms by combining the concept of wavelet packet transform (WPT), empirical mode decomposition (EMD) and ...
Koushik Maharatna +7 more
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Identification Of Electrode Enchepalo Graph Signal To Move The Cursor Using Back Propagation Method [PDF]
, 2012 In this paper the researchers describe the application of back propagation neural networks as classification and sampling technique (TS) for feature extraction of waveform signals Electro Enchepalo Graph (EEG).
Mauridhi Hery , Purnomo +2 more
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Quantitative EEG based on Renyi Entropy for Epileptic Classification [PDF]
Journal of Electrical and Electronics Engineering, 2019 Analysis on Electroencephalogram (EEG) signal can provide important information related to the clinical pathology of epilepsy. Detecting the onset, prediction and type of seizures based on EEG signals is very important to determine an appropriate ...
HADIYOSO Sugondo +2 more
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Brain Machine Interface: Analysis of segmented EEG Signal Classification Using Short-Time PCA and Recurrent Neural Networks [PDF]
Iraqi Journal for Electrical and Electronic Engineering, 2008 Brain machine interface provides a communication channel between the human brain and an external device. Brain interfaces are studied to provide rehabilitation to patients with neurodegenerative diseases; such patients loose all communication pathways ...
C. R. Hema +4 more
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A Framework for Schizophrenia EEG Signal Classification With Nature Inspired Optimization Algorithms
IEEE Access, 2020 One of the severe and prolonged disorder of the human brain which disturbs the behavioral characteristics of an individual completely such as interruption in the thinking process and speech is schizophrenia. It is a manifestation of many symptoms such as
S. Prabhakar +2 more
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A first attempt at constructing genetic programming expressions for EEG classification [PDF]
, 2005 Proceeding of: 15th International Conference on Artificial Neural Networks ICANN 2005, Poland, 11-15 September, 2005In BCI (Brain Computer Interface) research, the classification of EEG signals is a domain where raw data has to undergo some preprocessing,
Estébanez Tascón, César +8 more
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Brain Informatics, 2017 Classification of different mental tasks using electroencephalogram (EEG) signal plays an imperative part in various brain–computer interface (BCI) applications.
M. M. Rahman +2 more
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