Latest AI and machine learning research in seizures for healthcare professionals.
Forecasting electroencephalography (EEG) signals, that is, estimating future values of the time series based on the past ones, is essential in many real-time EEG-based applications, such as brain-computer interfaces and closed-loop brain stimulation. As these applications are becoming more and more common, the importance of a good prediction model has increased. Previously, the autoregressive mode...
Drug-resistant focal epilepsy is associated with abnormalities in the brain in both grey matter (GM) and superficial white matter (SWM). However, it is unknown if both types of abnormalities are important in supporting seizures. Here, we test if surgical removal of GM and/or SWM abnormalities relates to post-surgical seizure outcome in people with temporal lobe epilepsy (TLE). We analyzed stru...
This study investigates EEG as a potential early biomarker by applying deep learning techniques to resting-state EEG recordings from 31 subjects (15...
We propose a novel dual-loop system that synergistically combines responsive neurostimulation (RNS) implants with artificial intelligence-driven wea...
Electroencephalography (EEG) serves as an essential diagnostic tool in neurology; however, its accurate manual interpretation is a time-intensive pr...
The application of machine learning (ML) to electroencephalography (EEG) has great potential to advance both neuroscientific research and clinical a...
Generative AI is transforming education by enabling personalized, on-demand learning experiences. However, AI tutors lack the ability to assess a le...
Children with neurodevelopmental disorders require timely intervention to improve long-term outcomes, yet early screening remains inaccessible in ma...
Autism Spectrum Disorder (ASD) is a disorder of brain growth with great variability whose clinical presentation initially shows up during early stages...
Electroencephalography (EEG) is widely used in neuroscience and clinical research for analyzing brain activity. While deep learning models such as E...
Emotional Recognition in Conversation (ERC) is an important method for diagnosing health conditions such as autism or depression, as well as underst...
The success of foundation models in natural language processing and computer vision has motivated similar approaches for general time series analysi...
Nanodevices that show the potential for non-linear transformation of electrical signals and various forms of memory can be successfully used in new ...
Dream narratives provide a unique window into human cognition and emotion, yet their systematic analysis using artificial intelligence has been unde...
Neurological disorders represent significant global health challenges, driving the advancement of brain signal analysis methods. Scalp electroenceph...
In this paper, we focus on the challenge of individual variability in affective brain-computer interfaces (aBCI), which employs electroencephalogram...
Zero-Input AI (ZIA) introduces a novel framework for human-computer interaction by enabling proactive intent prediction without explicit user comman...
Predicting seizure freedom is essential for tailoring epilepsy treatment. But accurate prediction remains challenging with traditional methods, espe...
This study introduces a specialized pipeline designed to classify the concentration state of an individual student during online learning sessions b...
Integration of Brain-Computer Interfaces (BCIs) and Generative Artificial Intelligence (GenAI) has opened new frontiers in brain signal decoding, en...