Results 21 to 30 of about 1,230,009 (250)
Few-Electrode EEG from the Wearable Devices Using Domain Adaptation for Depression Detection
Biosensors, 2022 Nowadays, major depressive disorder (MDD) has become a crucial mental disease that endangers human health. Good results have been achieved by electroencephalogram (EEG) signals in the detection of depression.
Wei Wu +4 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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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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A novel multi-class imbalanced EEG signals classification based on the adaptive synthetic sampling (ADASYN) approach [PDF]
PeerJ Computer Science, 2021 Background Brain signals (EEG—Electroencephalography) are a gold standard frequently used in epilepsy prediction. It is crucial to predict epilepsy, which is common in the community.
Adi Alhudhaif
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Conditional Adversarial Domain Adaptation Neural Network for Motor Imagery EEG Decoding
Entropy, 2020 Decoding motor imagery (MI) electroencephalogram (EEG) signals for brain-computer interfaces (BCIs) is a challenging task because of the severe non-stationarity of perceptual decision processes.
Xingliang Tang, Xianrui Zhang
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PLoS ONE, 2013 Recently, many lines of investigation in neuroscience and statistical physics have converged to raise the hypothesis that the underlying pattern of neuronal activation which results in electroencephalography (EEG) signals is nonlinear, with self-affine ...
Todd Zorick, Mark A Mandelkern
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Sensors, 2023 Analyzing unstable gait patterns from Electroencephalography (EEG) signals is vital to develop real-time brain-computer interface (BCI) systems to prevent falls and associated injuries. This study investigates the feasibility of classification algorithms
Rahul Soangra +4 more
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Automated Schizophrenia detection using local descriptors with EEG signals
, 2023 Schizophrenia (SZ) is a severe mental disorder characterized by behavioral imbalance and impaired cognitive ability. This paper proposes a local descriptors-based automated approach for SZ detection using electroencephalogram (EEG) signals. Specifically,
Rajesh, Kandala N.V.P.S. +4 more
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A New Method for EEG Compressive Sensing
Advances in Electrical and Computer Engineering, 2012 The paper investigates the possibility of using compressive sensing techniques for the acquisition and reconstruction of EEG signals containing the evoked potential P300. A method of EEG compressive sensing based on the physiological correlation of EEG
FIRA, M., GORAS, L.
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Resting state cortical EEG rhythms in Alzheimer's disease: toward EEG markers for clinical applications: a review [PDF]
, 2012 The human brain contains an intricate network of about 100 billion neurons. Aging of the brain is characterized by a combination of synaptic pruning, loss of cortico-cortical connections, and neuronal apoptosis that provoke an age-dependent decline of ...
Lizio, Roberta +25 more
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