AIMC Topic: Electroencephalography
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Dual Attention Relation Network With Fine-Tuning for Few-Shot EEG Motor Imagery Classification.
IEEE transactions on neural networks and learning systems
Oct 29, 2024
Recently, motor imagery (MI) electroencephalography (EEG) classification techniques using deep learning have shown improved performance over conventional techniques. However, improving the classification accuracy on unseen subjects is still challengi...
CNN-Informer: A hybrid deep learning model for seizure detection on long-term EEG.
Neural networks : the official journal of the International Neural Network Society
Oct 28, 2024
Timely detecting epileptic seizures can significantly reduce accidental injuries of epilepsy patients and offer a novel intervention approach to improve their quality of life. Investigation on seizure detection based on deep learning models has achie...
Deep Autoencoder for Real-Time Single-Channel EEG Cleaning and Its Smartphone Implementation Using TensorFlow Lite With Hardware/Software Acceleration.
IEEE transactions on bio-medical engineering
Oct 25, 2024
OBJECTIVE: To remove signal contamination in electroencephalogram (EEG) traces coming from ocular, motion, and muscular artifacts which degrade signal quality. To do this in real-time, with low computational overhead, on a mobile platform in a channe...
Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis.
Sensors (Basel, Switzerland)
Oct 23, 2024
The global prevalence of Major Depressive Disorder (MDD) is increasing at an alarming rate, underscoring the urgent need for timely and accurate diagnoses to facilitate effective interventions and treatments. Electroencephalography remains a widely u...
Transfer Learning With Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-Centre Data.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Oct 22, 2024
Machine learning and deep learning advancements have boosted Brain-Computer Interface (BCI) performance, but their wide-scale applicability is limited due to factors like individual health, hardware variations, and cultural differences affecting neur...
Attention Induced Dual Convolutional-Capsule Network (AIDC-CN): A deep learning framework for motor imagery classification.
Computers in biology and medicine
Oct 18, 2024
In recent times, Electroencephalography (EEG)-based motor imagery (MI) decoding has garnered significant attention due to its extensive applicability in healthcare, including areas such as assistive robotics and rehabilitation engineering. Neverthele...
An interpretable and generalizable deep learning model for iEEG-based seizure prediction using prototype learning and contrastive learning.
Computers in biology and medicine
Oct 17, 2024
Epileptic seizure prediction plays a crucial role in enhancing the quality of life for individuals with epilepsy. Over recent years, a multitude of deep learning-based approaches have emerged to tackle this challenging task, leading to significant ad...
Real-time sub-milliwatt epilepsy detection implemented on a spiking neural network edge inference processor.
Computers in biology and medicine
Oct 16, 2024
Analyzing electroencephalogram (EEG) signals to detect the epileptic seizure status of a subject presents a challenge to existing technologies aimed at providing timely and efficient diagnosis. In this study, we aimed to detect interictal and ictal p...
Novel multimodal sensing and machine learning strategies to classify cognitive workload in laparoscopic surgery.
European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
Oct 15, 2024
BACKGROUND: Surgeons can experience elevated cognitive workload (CWL) during surgery due to various factors including operative technicalities and the environmental demands of the operating theatre. This can result in poorer outcomes and have a detri...
Graph neural networks for electroencephalogram analysis: Alzheimer's disease and epilepsy use cases.
Neural networks : the official journal of the International Neural Network Society
Oct 15, 2024
Electroencephalography (EEG) is widely used as a non-invasive technique for the diagnosis of several brain disorders, including Alzheimer's disease and epilepsy. Until recently, diseases have been identified over EEG readings by human experts, which ...
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