AIMC Topic: Electroencephalography

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LGFormer: integrating local and global representations for EEG decoding.

Journal of neural engineering Apr 3, 2025
Electroencephalography (EEG) decoding is challenging because of its temporal variability and low signal-to-noise ratio, which complicate the extraction of meaningful information from signals. Although convolutional neural networks (CNNs) effectively ...
Humans Neural Networks, Computer Electroencephalography Brain-Computer Interfaces Brain
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Hardware Optimization and Implementation of a 16-Channel Neural Tree Classifier for On-Chip Closed-Loop Neuromodulation.

IEEE transactions on biomedical circuits and systems Apr 2, 2025
This work presents the development of on-chip machine learning (ML) classifiers for implantable neuromodulation system-on-chips (SoCs), aimed at detecting epileptic seizures for closed-loop neuromodulation applications. Tree-based classifiers have ga...
Machine Learning Algorithms Humans Neural Networks, Computer Seizures Signal Processing, Computer-Assisted Electroencephalography Epilepsy
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Updates in Neonatal Seizures.

Clinics in perinatology Apr 1, 2025
Neonatal seizures are a common medical emergency, necessitating prompt treatment. The most common etiologies include hypoxic-ischemic encephalopathy, ischemic stroke, and intracranial hemorrhage, with numerous other uncommon etiologies. Accurate diag...
Humans Seizures Anticonvulsants Phenobarbital Hypoxia-Ischemia, Brain Infant, Newborn Electroencephalography Artificial Intelligence Prognosis
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Shared autonomy between human electroencephalography and TD3 deep reinforcement learning: A multi-agent copilot approach.

Annals of the New York Academy of Sciences Mar 30, 2025
Deep reinforcement learning (RL) algorithms enable the development of fully autonomous agents that can interact with the environment. Brain-computer interface (BCI) systems decipher human implicit brain signals regardless of the explicit environment....
Electroencephalography Brain-Computer Interfaces Brain Reinforcement, Psychology Reinforcement Machine Learning Humans Deep Learning Algorithms
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Efficient Seizure Detection by Complementary Integration of Convolutional Neural Network and Vision Transformer.

International journal of neural systems Mar 29, 2025
Epilepsy, as a prevalent neurological disorder, is characterized by its high incidence, sudden onset, and recurrent nature. The development of an accurate and real-time automatic seizure detection system is crucial for assisting clinicians in making ...
Seizures Signal Processing, Computer-Assisted Convolutional Neural Networks Electroencephalography Humans
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Artificial intelligence applied to electroencephalography in epilepsy.

Revue neurologique Mar 29, 2025
Artificial intelligence (AI) is progressively transforming all fields of medicine, promising substantial changes in clinical practice. In the context of epilepsy, electroencephalography (EEG), a technique used for over a century, has historically bee...
Electroencephalography Epilepsy Humans Artificial Intelligence Algorithms
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Flexible Patched Brain Transformer model for EEG decoding.

Scientific reports Mar 29, 2025
Decoding the human brain using non-invasive methods is a significant challenge. This study aims to enhance electroencephalography (EEG) decoding by developing of machine learning methods. Specifically, we propose the novel, attention-based Patched Br...
Signal Processing, Computer-Assisted Algorithms Humans Brain Electroencephalography Machine Learning
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Enhancing convolutional neural networks in electroencephalogram driver drowsiness detection using human inspired optimizers.

Scientific reports Mar 29, 2025
Driver drowsiness is a significant safety concern, contributing to numerous traffic accidents. To address this issue, researchers have explored electroencephalogram (EEG)-based detection systems. Due to the high-dimensional nature of EEG signals and ...
Adult Male Electroencephalography Accidents, Traffic Deep Learning Neural Networks, Computer Automobile Driving Humans Algorithms Sleep Stages
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Electroencephalography Decoding with Conditional Identification Generator.

International journal of neural systems Mar 27, 2025
Decoding Electroencephalography (EEG) signals are extremely useful for advancing and understanding human-artificial intelligence (AI) interaction systems. Recent advancements in deep neural networks (DNNs) have demonstrated significant promise in thi...
Humans Brain Neural Networks, Computer Deep Learning Artificial Intelligence Electroencephalography
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Recurrent and convolutional neural networks in classification of EEG signal for guided imagery and mental workload detection.

Scientific reports Mar 27, 2025
The Guided Imagery technique is reported to be used by therapists all over the world in order to increase the comfort of patients suffering from a variety of disorders from mental to oncology ones and proved to be successful in numerous of ways. Poss...
Workload Imagery, Psychotherapy Signal Processing, Computer-Assisted Memory, Short-Term Young Adult Humans Adult Neural Networks, Computer Deep Learning Female Electroencephalography Male
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