AIMC Topic: Electroencephalography

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Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework.

Nature communications Apr 16, 2024
Interictal Epileptiform Discharges (IED) and High Frequency Oscillations (HFO) in intraoperative electrocorticography (ECoG) may guide the surgeon by delineating the epileptogenic zone. We designed a modular spiking neural network (SNN) in a mixed-si...
Humans Neural Networks, Computer Electrocorticography Electroencephalography
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Editorial for the Special Issue "Sensing Brain Activity Using EEG and Machine Learning".

Sensors (Basel, Switzerland) Apr 15, 2024
Sensing brain activity to reveal, analyze and recognize brain activity patterns has become a topic of great interest and ongoing research [...].
Brain Electroencephalography Humans Machine Learning
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vEpiNet: A multimodal interictal epileptiform discharge detection method based on video and electroencephalogram data.

Neural networks : the official journal of the International Neural Network Society Apr 14, 2024
To enhance deep learning-based automated interictal epileptiform discharge (IED) detection, this study proposes a multimodal method, vEpiNet, that leverages video and electroencephalogram (EEG) data. Datasets comprise 24 931 IED (from 484 patients) a...
Male Middle Aged Deep Learning Adolescent Young Adult Adult Neural Networks, Computer Female Epilepsy Video Recording Humans Child Electroencephalography
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Explainable Deep-Learning Prediction for Brain-Computer Interfaces Supported Lower Extremity Motor Gains Based on Multistate Fusion.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Apr 12, 2024
Predicting the potential for recovery of motor function in stroke patients who undergo specific rehabilitation treatments is an important and major challenge. Recently, electroencephalography (EEG) has shown potential in helping to determine the rela...
Brain-Computer Interfaces Electroencephalography Stroke Stroke Rehabilitation Humans Deep Learning
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Enhancing cross-subject EEG emotion recognition through multi-source manifold metric transfer learning.

Computers in biology and medicine Apr 9, 2024
Transfer learning (TL) has demonstrated its efficacy in addressing the cross-subject domain adaptation challenges in affective brain-computer interfaces (aBCI). However, previous TL methods usually use a stationary distance, such as Euclidean distanc...
Brain-Computer Interfaces Male Emotions Electroencephalography Signal Processing, Computer-Assisted Brain Female Machine Learning Algorithms Humans
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An efficient Parkinson's disease detection framework: Leveraging time-frequency representation and AlexNet convolutional neural network.

Computers in biology and medicine Apr 9, 2024
Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the quality of life of over 10 million individuals worldwide. Early diagnosis is crucial for timely intervention and better patient outcomes. Electroencephalogram (EEG) si...
Neural Networks, Computer Brain Humans Electroencephalography Signal Processing, Computer-Assisted Male Parkinson Disease
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Brain-machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study.

Journal of neuroengineering and rehabilitation Apr 5, 2024
BACKGROUND: This research focused on the development of a motor imagery (MI) based brain-machine interface (BMI) using deep learning algorithms to control a lower-limb robotic exoskeleton. The study aimed to overcome the limitations of traditional BM...
Humans Electroencephalography Algorithms Brain-Computer Interfaces Deep Learning Exoskeleton Device Lower Extremity
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Harnessing machine learning for EEG signal analysis: Innovations in depth of anaesthesia assessment.

Artificial intelligence in medicine Apr 4, 2024
Anaesthesia, crucial to surgical practice, is undergoing renewed scrutiny due to the integration of artificial intelligence in its medical use. The precise control over the temporary loss of consciousness is vital to ensure safe, pain-free procedures...
Humans Anesthesia Consciousness Monitors Electroencephalography Signal Processing, Computer-Assisted Machine Learning Algorithms
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Manifold Learning-Based Common Spatial Pattern for EEG Signal Classification.

IEEE journal of biomedical and health informatics Apr 4, 2024
EEG signal classification using Riemannian manifolds has shown great potential. However, the huge computational cost associated with Riemannian metrics poses challenges for applying Riemannian methods, particularly in high-dimensional feature data. T...
Machine Learning Humans Algorithms Electroencephalography
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A machine learning based depression screening framework using temporal domain features of the electroencephalography signals.

PloS one Mar 27, 2024
Depression is a serious mental health disorder affecting millions of individuals worldwide. Timely and precise recognition of depression is vital for appropriate mediation and effective treatment. Electroencephalography (EEG) has surfaced as a promis...
Support Vector Machine Electroencephalography Algorithms Depression Humans Machine Learning
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