211 Result(s)
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Article
Time–frequency–space transformer EEG decoding for spinal cord injury
Transformer neural networks based on multi-head self-attention are effective in several fields. To capture brain activity on electroencephalographic (EEG) signals and construct an effective pattern recognition...
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Functional connectivity of EEG motor rhythms after spinal cord injury
Spinal cord injury (SCI), which is the injury of the spinal cord site resulting in motor dysfunction, has prompted the use of motor imagery (MI)-based brain computer interface (BCI) systems for motor function ...
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Quantitative analysis and machine learning-based interpretation of EEG signals in coma and brain-death diagnosis
Electroencephalography (EEG) reflects brain activity and is crucial for diagnosing states such as coma and brain-death. However, the clinical interpretation of EEG signals faces challenges due to the patients’...
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Recognition of autism spectrum disorder in children based on electroencephalogram network topology
Although our knowledge of autism spectrum disorder (ASD) has been deepened, the accurate diagnosis of ASD from normal individuals is still left behind. In this study, we proposed to apply the spatial pattern o...
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Multimodal and hemispheric graph-theoretical brain network predictors of learning efficacy for frontal alpha asymmetry neurofeedback
EEG neurofeedback using frontal alpha asymmetry (FAA) has been widely used for emotion regulation, but its effectiveness is controversial. Studies indicated that individual differences in neurofeedback trainin...
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An efficient memory reserving-and-fading strategy for vector quantization based 3D brain segmentation and tumor extraction using an unsupervised deep learning network
Deep learning networks are state-of-the-art approaches for 3D brain image segmentation, and the radiological characteristics extracted from tumors are of great significance for clinical diagnosis, treatment pl...
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Open AccessTemporal-spatial deciphering mental subtraction in the human brain
Mental subtraction, involving numerical processing and operation, requires a complex interplay among several brain regions. Diverse studies have utilized scalp electroencephalograph, electrocorticogram, or fun...
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Machine to brain: facial expression recognition using brain machine generative adversarial networks
The human brain can effectively perform Facial Expression Recognition (FER) with a few samples by utilizing its cognitive ability. However, unlike the human brain, even the well-trained deep neural network is ...
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Open AccessModeling the grid cell activity based on cognitive space transformation
The grid cells in the medial entorhinal cortex are widely recognized as a critical component of spatial cognition within the entorhinal-hippocampal neuronal circuits. To account for the hexagonal patterns, sev...
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Short-length SSVEP data extension by a novel generative adversarial networks based framework
Steady-state visual evoked potentials (SSVEPs) based brain–computer interface (BCI) has received considerable attention due to its high information transfer rate (ITR) and available quantity of targets. Howeve...
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Development of a humanoid robot control system based on AR-BCI and SLAM navigation
Brain-computer interface (BCI)-based robot combines BCI and robotics technology to realize the brain’s intention to control the robot, which not only opens up a new way for the daily care of the disabled indiv...
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Research progress of epileptic seizure prediction methods based on EEG
At present, at least 30% of refractory epilepsy patients in the world cannot be effectively controlled and treated. The suddenness and unpredictability of seizures greatly affect the physical and mental health...
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Decoded EEG neurofeedback-guided cognitive reappraisal training for emotion regulation
Neurofeedback, when combined with cognitive reappraisal, offers promising potential for emotion regulation training. However, prior studies have predominantly relied on functional magnetic resonance imaging, w...
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The effects of implicit emotion on the use of theory of mind among college students in China
This research aims to study the impact of implicit emotion on the use of theory of mind and enrich the research on emotions and the use of theory of mind, thus allowing adults to apply theory of mind more effe...
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Deep-learning-optimized microstate network analysis for early Parkinson’s disease with mild cognitive impairment
Graph-theory-based topological impairment of the whole-brain network has been verified to be one of the characteristics of mild cognitive impairment (MCI). However, two major challenges impede the further unde...
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An improved CapsNet based on data augmentation for driver vigilance estimation with forehead single-channel EEG
Various studies have shown that it is necessary to estimate the drivers’ vigilance to reduce the occurrence of traffic accidents. Most existing EEG-based vigilance estimation studies have been performed on int...
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The role of extracellular glutamate homeostasis dysregulated by astrocyte in epileptic discharges: a model evidence
Glutamate (Glu) is a predominant excitatory neurotransmitter that acts on glutamate receptors to transfer signals in the central nervous system. Abnormally elevated extracellular glutamate levels is closely re...
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Dynamic functional connectivity correlates of mental workload
Tasks with high mental workload often involve higher cognitive functions of the human brain and complex information flow involving multiple brain regions. However, the dynamics of functional connectivity betwe...
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Dynamics of interaction between IH and IKLT currents to mediate double resonances of medial superior olive neurons related to sound localization
Neurons in the medial superior olive (MSO) exhibit high frequency responses such as subthreshold resonance, which is helpful to sensitively detect a small difference in the arrival time of sounds between two e...
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How does the human brain process noisy speech in real life? Insights from the second-person neuroscience perspective
Comprehending speech with the existence of background noise is of great importance for human life. In the past decades, a large number of psychological, cognitive and neuroscientific research has explored the ...
