Accurate recognition and classification of motor imagery electroencephalogram (MI-EEG) signals are crucial for the successful implementation of brain-computer interfaces (BCI). However, inherent characteristics in original MI-EEG signals, such as non...
Research on emotion recognition is an interesting area because of its wide-ranging applications in education, marketing, and medical fields. This study proposes a multi-branch convolutional neural network model based on cross-attention mechanism (MCN...
BACKGROUND: Alzheimer's disease (AD) and frontotemporal dementia (FTD) are both progressive neurological disorders that affect the elderly. Distinguishing between individuals suffering from these two diseases in the early stages can be quite challeng...
EEG signals exhibit spatio-temporal characteristics due to the neural activity dispersion in space over the brain and the dynamic temporal patterns of electrical activity in neurons. This study tries to effectively utilize the spatio-temporal nature ...
Brain-controlled robotic arm systems are designed to provide a method of communication and control for individuals with limited mobility or communication abilities. These systems can be beneficial for people who have suffered from a spinal cord injur...
Medical & biological engineering & computing
Jan 30, 2025
With the advancement of artificial intelligence technology, more and more effective methods are being used to identify and classify Electroencephalography (EEG) signals to address challenges in healthcare and brain-computer interface fields. The appl...
PURPOSE: In the context of EEG-based emotion recognition tasks, a conventional strategy involves the extraction of spatial and temporal features, subsequently fused for emotion prediction. However, due to the pronounced individual variability in EEG ...
EEG involves recording electrical activity generated by the brain through electrodes placed on the scalp. Imagined speech classification has emerged as an essential area of research in brain-computer interfaces (BCIs). Despite significant advances, a...
Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications (ASMs), a condition known as pharmacoresistant epilepsy. The management of pharmacoresistant epilepsy remains an intractable issue in the clinic. Its ...
PURPOSE: Advancements in Machine Learning (ML) techniques have revolutionized diagnosing and monitoring epileptic seizures using Electroencephalogram (EEG) signals. This analysis aims to determine the effectiveness of ML techniques in recognizing pat...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.