AIMC Topic: Electroencephalography
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Real-Time Epileptic Seizure Prediction Method With Spatio-Temporal Information Transfer Learning.
IEEE journal of biomedical and health informatics
Mar 6, 2025
Despite numerous studies aimed at improving accuracy, the accurate prediction of epileptic seizures remains a challenge in clinical practice due to the high computational cost, poor real-time performance, and over-reliance on labelled data. To addres...
AdaptEEG: A Deep Subdomain Adaptation Network With Class Confusion Loss for Cross-Subject Mental Workload Classification.
IEEE journal of biomedical and health informatics
Mar 6, 2025
EEG signals exhibit non-stationary characteristics, particularly across different subjects, which presents significant challenges in the precise classification of mental workload levels when applying a trained model to new subjects. Domain adaptation...
EEG-Deformer: A Dense Convolutional Transformer for Brain-Computer Interfaces.
IEEE journal of biomedical and health informatics
Mar 6, 2025
Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is challenging yet essential for decoding brain activities using brain-computer interfaces (BCIs). Although Transformers are popular for their long-term sequential learn...
REI-Net: A Reference Electrode Standardization Interpolation Technique Based 3D CNN for Motor Imagery Classification.
IEEE journal of biomedical and health informatics
Mar 6, 2025
High-quality scalp EEG datasets are extremely valuable for motor imagery (MI) analysis. However, due to electrode size and montage, different datasets inevitably experience channel information loss, posing a significant challenge for MI decoding. A 2...
Near-lossless EEG signal compression using a convolutional autoencoder: Case study for 256-channel binocular rivalry dataset.
Computers in biology and medicine
Mar 5, 2025
Electroencephalography (EEG) experiments typically generate vast amounts of data due to the high sampling rates and the use of multiple electrodes to capture brain activity. Consequently, storing and transmitting these large datasets is challenging, ...
Unlocking new frontiers in epilepsy through AI: From seizure prediction to personalized medicine.
Epilepsy & behavior : E&B
Mar 4, 2025
Artificial intelligence (AI) is revolutionizing epilepsy care by advancing seizure detection, enhancing diagnostic precision, and enabling personalized treatment. Machine learning and deep learning technologies improve seizure monitoring, automate EE...
An Efficient Approach for Detection of Various Epileptic Waves Having Diverse Forms in Long Term EEG Based on Deep Learning.
Brain topography
Mar 4, 2025
EEG is the most powerful tool for epilepsy discharge detection in brain. Visual evaluation is hard in long term monitoring EEG data as huge amount of data needs to be inspected. Considering the fast and efficient results from deep learning networks e...
SeizyML: An Application for Semi-Automated Seizure Detection Using Interpretable Machine Learning Models.
Neuroinformatics
Mar 3, 2025
Despite the vast number of publications reporting seizures and the reliance of the field on accurate seizure detection, there is a lack of open-source software tools in the scientific community for automating seizure detection based on electrographic...
A hybrid network based on multi-scale convolutional neural network and bidirectional gated recurrent unit for EEG denoising.
Neuroscience
Feb 28, 2025
Electroencephalogram (EEG) signals are time series data containing abundant brain information. However, EEG frequently contains various artifacts, such as electromyographic, electrooculographic, and electrocardiographic. These artifacts can change EE...
Machine learning analysis of cortical activity in visual associative learning tasks with differing stimulus complexity.
Physiology international
Feb 27, 2025
Associative learning tests are cognitive assessments that evaluate the ability of individuals to learn and remember relationships between pairs of stimuli. The Rutgers Acquired Equivalence Test (RAET) is an associative learning test that utilizes ima...
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