AIMC Topic: Electroencephalography

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Enhancing the performance of SSVEP-based BCIs by combining task-related component analysis and deep neural network.

Scientific reports Jan 2, 2025
Steady-State Visually Evoked Potential (SSVEP) signals can be decoded by either a traditional machine learning algorithm or a deep learning network. Combining the two methods is expected to enhance the performance of an SSVEP-based brain-computer int...
Humans Neural Networks, Computer Signal Processing, Computer-Assisted Brain-Computer Interfaces Evoked Potentials, Visual Algorithms Deep Learning Electroencephalography
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A Novel State Space Model with Dynamic Graphic Neural Network for EEG Event Detection.

International journal of neural systems Dec 31, 2024
Electroencephalography (EEG) is a widely used physiological signal to obtain information of brain activity, and its automatic detection holds significant research importance, which saves doctors' time, improves detection efficiency and accuracy. Howe...
Electroencephalography Humans Sleep Stages Seizures Neural Networks, Computer Signal Processing, Computer-Assisted Brain
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Virtual reality-assisted prediction of adult ADHD based on eye tracking, EEG, actigraphy and behavioral indices: a machine learning analysis of independent training and test samples.

Translational psychiatry Dec 31, 2024
Given the heterogeneous nature of attention-deficit/hyperactivity disorder (ADHD) and the absence of established biomarkers, accurate diagnosis and effective treatment remain a challenge in clinical practice. This study investigates the predictive ut...
Male Eye-Tracking Technology Virtual Reality Support Vector Machine Electroencephalography Attention Humans Female Actigraphy Machine Learning Young Adult Attention Deficit Disorder with Hyperactivity Adult
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EEG-based emotion recognition using multi-scale dynamic CNN and gated transformer.

Scientific reports Dec 28, 2024
Emotions play a crucial role in human thoughts, cognitive processes, and decision-making. EEG has become a widely utilized tool in emotion recognition due to its high temporal resolution, real-time monitoring capabilities, portability, and cost-effec...
Emotions Electroencephalography Signal Processing, Computer-Assisted Brain-Computer Interfaces Humans Neural Networks, Computer Algorithms
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Decoding of pain during heel lancing in human neonates with EEG signal and machine learning approach.

Scientific reports Dec 28, 2024
Currently, pain assessment using electroencephalogram signals and machine learning methods in clinical studies is of great importance, especially for those who cannot express their pain. Since newborns are among the high-risk group and always experie...
Support Vector Machine Pain Measurement Electroencephalography Female Infant, Newborn Male Pain Heel Humans Machine Learning
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Performance investigation of MVMD-MSI algorithm in frequency recognition for SSVEP-based brain-computer interface and its application in robotic arm control.

Medical & biological engineering & computing Dec 27, 2024
This study focuses on improving the performance of steady-state visual evoked potential (SSVEP) in brain-computer interfaces (BCIs) for robotic control systems. The challenge lies in effectively reducing the impact of artifacts on raw data to enhance...
Algorithms Female Young Adult Humans Brain-Computer Interfaces Evoked Potentials, Visual Electroencephalography Signal Processing, Computer-Assisted Arm Adult Male Robotics
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Emotion recognition using multi-scale EEG features through graph convolutional attention network.

Neural networks : the official journal of the International Neural Network Society Dec 27, 2024
Emotion recognition via electroencephalogram (EEG) signals holds significant promise across various domains, including the detection of emotions in patients with consciousness disorders, assisting in the diagnosis of depression, and assessing cogniti...
Brain-Computer Interfaces Emotions Electroencephalography Attention Humans Neural Networks, Computer
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A novel way to use cross-validation to measure connectivity by machine learning allows epilepsy surgery outcome prediction.

NeuroImage Dec 27, 2024
The rate of success of epilepsy surgery, ensuring seizure-freedom, is limited by the lack of epileptogenicity biomarkers. Previous evidence supports the critical role of functional connectivity during seizure generation to characterize the epileptoge...
Epilepsy Nerve Net Electrocorticography Young Adult Adult Male Machine Learning Adolescent Brain Treatment Outcome Humans Female Electroencephalography
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Preictal period optimization for deep learning-based epileptic seizure prediction.

Journal of neural engineering Dec 27, 2024
. Accurate seizure prediction could prove critical for improving patient safety and quality of life in drug-resistant epilepsy. While deep learning-based approaches have shown promising performance using scalp electroencephalogram (EEG) signals, the ...
Seizures Humans Female Deep Learning Adult Electroencephalography Male Epilepsy
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Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism.

Mathematical biosciences and engineering : MBE Dec 25, 2024
Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis re...
Humans Seizures Pattern Recognition, Automated Neural Networks, Computer Signal Processing, Computer-Assisted Machine Learning Attention Deep Learning Algorithms Electroencephalography Sensitivity and Specificity Epilepsy
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