Latest AI and machine learning research in seizures for healthcare professionals.
Electroencephalogram (EEG) artifact removal has been investigated for decades with the goal of reconstructing the clean signals for the subsequent EEG analysis. However, existing denoising methods still have limited capabilities to handle the highly mixed artifacts and the fine-grained temporal dependency of artifact-free EEG without a priori knowledge of the artifacts. To address the challenges, ...
Granger causality (GC) effective connectivity (EC) calculated from electroencephalogram (EEG) signals has been widely used in mental disorder detection. However, the existing methods only take into account linear dynamics or nonlinear dynamics within a single sample, ignoring the nonlinear dynamics shared by the same class of subjects. In this article, a model combining graph neural networks (GNNs...
Integrating prior knowledge of neurophysiology into neural network architecture enhances the performance of emotion decoding. While numerous technique...
The contemporary diagnosis of Major Depressive Disorder (MDD) primarily relies on subjective assessments and self-reported measures, often resulting i...
Seizures in electroencephalogram (EEG) data constitute a special case of sub-sequence anomalies in multivariate data with numerous challenges. These c...
In this paper, a hybrid CNN-BiLSTM model for EEG-based emotion detection system is presented. The proposed technique is developed by extracting featur...
PURPOSE: Focal cortical dysplasia (FCD) is a common cause of pharmacoresistant epilepsy. However, it can be challenging to detect FCD using MRI alone....
Transfer learning, a technique commonly used in generative artificial intelligence, allows neural network models to bring prior knowledge to bear wh...
The automatic classification of medical time series signals, such as electroencephalogram (EEG) and electrocardiogram (ECG), plays a pivotal role in...
Reconstructing visual stimuli from EEG signals is a crucial step in realizing brain-computer interfaces. In this paper, we propose a transformer-bas...
Accurately localizing the brain regions that triggers seizures and predicting whether a patient will be seizure-free after surgery are vital for sur...
Recent advancements in Large Language Models have inspired the development of foundation models across various domains. In this study, we evaluate t...
Recent studies have demonstrated that the representations of artificial neural networks (ANNs) can exhibit notable similarities to cortical representa...
Data augmentation has been demonstrated to improve the classification accuracy of deep learning models in steady-state visual evoked potential-based b...
Reconstructing and understanding dynamic visual information (video) from brain EEG recordings is challenging due to the non-stationary nature of EEG...
Human preference research is a significant domain in psychology and psychophysiology, with broad applications in psychiatric evaluation and daily li...
Aperiodic neural activity has been the subject of intense research interest lately as it could reflect on the cortical excitation/inhibition ratio, ...
Aperiodic neural activity has been the subject of intense research interest lately as it could reflect on the cortical excitation/inhibition ratio, ...
Feature engineering for generalized seizure detection models remains a significant challenge. Recently proposed models show variable performance dep...
Functional connectivity (FC) analyses of intracranial EEG (iEEG) signals can potentially improve the mapping of epileptic networks in drug-resistant f...