Deep learning of symbolic EEG patterns detects ADHD with 96% accuracy in children
A new EEG study combined entropy-optimized symbolic signal analysis with a bidirectional LSTM network to distinguish children with ADHD from ...
A new EEG study combined entropy-optimized symbolic signal analysis with a bidirectional LSTM network to distinguish children with ADHD from ...
MoSeq behavioral segmentation reveals a robust behavioral signature of trihexyphenidyl in TOR1A dystonia mice and exposes sex differences invisible to ...
A 5.6-microsecond molecular dynamics study combined with machine learning has mapped the dynamic network of residues that control opening and ...
A new deep learning framework called EEG-SDL-Net combines Kolmogorov–Arnold networks, Sobel-based temporal enhancement, and channel attention to classify EEG signals ...
In recent years, the quest to objectively quantify mental health conditions has gained enormous momentum, fueled by advances in artificial ...
In a groundbreaking advancement at the intersection of neuroscience, artificial intelligence, and movement disorder therapy, researchers have unveiled a novel ...
In the evolving landscape of neuroscience, one of the greatest challenges is deciphering the complex electrical symphony played by diverse ...
In a striking leap forward for personalized medicine, researchers from Japan’s National Center of Neurology and Psychiatry along with Tohoku ...
A groundbreaking study led by researchers at the University of California, Irvine, has unveiled the most comprehensive gene regulatory maps ...
In a groundbreaking study that promises to revolutionize the field of neuroimaging, researchers have introduced a novel approach to voxel-based ...
© 2025 Scienmag - Science Magazine
© 2025 Scienmag - Science Magazine