Results 21 to 30 of about 3,887,920 (279)
Journal of King Saud University: Computer and Information Sciences, 2021 Electroencephalography (EEG) is a measurement tool to measure the electrical activity of brain observed due to chemical variation in brain. The EEG analysis has important role in feature extraction and classification methods for detecting and predicting ...
Varsha Harpale, Vinayak Bairagi
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Federated Transfer Learning for EEG Signal Classification [PDF]
Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2020 The success of deep learning (DL) methods in the Brain-Computer Interfaces (BCI) field for classification of electroencephalographic (EEG) recordings has been restricted by the lack of large datasets.
Ce Ju +5 more
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IEEE Access, 2020 As one of the most important research fields in the brain-computer interface (BCI) field, electroencephalogram (EEG) classification has a wide range of application values.
Wenkai Huang +3 more
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Motor Imagery EEG Signal Recognition Using Deep Convolution Neural Network
Frontiers in Neuroscience, 2021 Brain computer interaction (BCI) based on EEG can help patients with limb dyskinesia to carry out daily life and rehabilitation training. However, due to the low signal-to-noise ratio and large individual differences, EEG feature extraction and ...
Xiongliang Xiao, Yuee Fang
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Application and Development of EEG Acquisition and Feedback Technology: A Review
Biosensors, 2023 This review focuses on electroencephalogram (EEG) acquisition and feedback technology and its core elements, including the composition and principles of the acquisition devices, a wide range of applications, and commonly used EEG signal classification ...
Yong Qin +4 more
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Selection of optimal wavelet features for epileptic EEG signal classification with LSTM
Neural computing & applications (Print), 2021 Epilepsy remains one of the most common chronic neurological disorders; hence, there is a need to further investigate various models for automatic detection of seizure activity. An effective detection model can be achieved by minimizing the complexity of
I. Aliyu, C. Lim
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Frontiers in Neuroscience, 2023 IntroductionAs an important human-computer interaction technology, steady-state visual evoked potential (SSVEP) plays a key role in the application of brain computer interface (BCI) systems by accurately decoding SSVEP signals.
Wenqiang Yan +3 more
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EEG Signal Classification Based On Fuzzy Classifiers
IEEE Transactions on Industrial Informatics, 2021 Electroencephalogram (EEG) signal classification is used in many applications. Typically, this classification is implemented based on methods which consist of two steps.
J. Rabcan +3 more
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Discriminative Tandem Features for HMM-based EEG Classification [PDF]
, 2013 We investigate the use of discriminative feature extractors in tandem configuration with generative EEG classification system. Existing studies on dynamic EEG classification typically use hidden Markov models (HMMs) which lack discriminative capability ...
Ting, Chee-Ming +8 more
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IEEE Access, 2019 This study presents an automatic sleep-stage classification system based on utilizing compressive sensing (CS) for data reduction. The amount of electroencephalogram (EEG) signal data required for sleep-stage classification can be significantly reduced ...
Hyunkeun Lee +4 more
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