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The Relationship Between the Functional Integration and Topological Complexity of Human Electroencephalographic (EEG) Signals: Three Moderating Factors [PDF]
Brain SciencesBackground and Objectives: A useful strategy to study the dynamic functioning of the human brain is to focus on the relationship between the brain’s functional integration and topological complexity.
Logan T. Trujillo
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Entropy, 2021 This paper analyses the complexity of electroencephalogram (EEG) signals in different temporal scales for the analysis and classification of focal and non-focal EEG signals.
Marta Borowska
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Journal of King Saud University: Computer and Information Sciences, 2022 Automatic epilepsy detection from electroencephalogram (EEG) signals is an alternative to manual detection performed by a human expert. High classification performance is needed in automatic epilepsy detection from EEG signals to avoid miss detection ...
Dwi Sunaryono +2 more
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Multiscale permutation Rényi entropy and its application for EEG signals. [PDF]
PLoS ONE, 2018 There is considerable interest in analyzing the complexity of electroencephalography (EEG) signals. However, some traditional complexity measure algorithms only quantify the complexities of signals, but cannot discriminate different signals very well. To
Yinghuang Yin, Kehui Sun, Shaobo He
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IEEE Access, 2021 The current clinically used electroencephalography (EEG) sensors are not self-applicable. This complicates the recording of the brain’s electrical activity in unattended home polysomnography (PSG).
Matias Rusanen +8 more
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A Survey on Denoising Techniques of Electroencephalogram Signals Using Wavelet Transform
Signals, 2022 Electroencephalogram (EEG) artifacts such as eyeblink, eye movement, and muscle movements widely contaminate the EEG signals. Those unwanted artifacts corrupt the information contained in the EEG signals and degrade the performance of qualitative ...
Maximilian Grobbelaar +6 more
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A Novel Method of Emotion Recognition from Multi-Band EEG Topology Maps Based on ERENet
Applied Sciences, 2022 EEG-based emotion recognition research has become a hot research topic. However, many studies focus on identifying emotional states from time domain features, frequency domain features, and time-frequency domain features of EEG signals, ignoring the ...
Ziyi Lv +2 more
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Electroencephalogram-Based Motor Imagery Classification Using Deep Residual Convolutional Networks
Frontiers in Neuroscience, 2021 The classification of electroencephalogram (EEG) signals is of significant importance in brain-computer interface (BCI) systems. Aiming to achieve intelligent classification of motor imagery EEG types with high accuracy, a classification methodology ...
Jing-Shan Huang +10 more
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FGANet: fNIRS-Guided Attention Network for Hybrid EEG-fNIRS Brain-Computer Interfaces
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022 Non-invasive brain-computer interfaces (BCIs) have been widely used for neural decoding, linking neural signals to control devices. Hybrid BCI systems using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have received ...
Youngchul Kwak +2 more
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Person-identifying brainprints are stably embedded in EEG mindprints
Scientific Reports, 2022 Electroencephalography (EEG) signals measured under fixed conditions have been exploited as biometric identifiers. However, what contributes to the uniqueness of one's brain signals remains unclear.
Yao-Yuan Yang +3 more
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