Abstract: Domain adaptation has proven effective for suppressing the inter-subject variability problem in cross-subject EEG classification tasks in which labeled data is available for source subjects ...
For many families, a child’s first seizure feels like the ground has slipped away. One moment, everything is normal. The next, there is shaking, stari.
QuantalX combined transcranial magnetic stimulation and EEG technology in its first-of-its-kind brain network function ...
Li and colleagues developed a deep-learning model to analyze EEG recordings and detect event-level EEG spikes. 2. The model achieved high accuracy and a low false-positive rate, with only 32% of human ...
Moving cannabis to a category of drugs that includes some common medicines will have implications for research, businesses and patients. By Jan Hoffman President Trump on Thursday ordered cannabis to ...
In this study, researchers developed a deep learning framework to analyse EEG signals from individuals with Alzheimer’s disease, frontotemporal dementia, and cognitively normal controls. The model ...
Abstract: Deep learning has significantly enhanced the research on the emerging issue of Electroencephalogram (EEG)-based visual classification and reconstruction, which has gained a growth of ...
Early and objective screening for major depressive disorder (MDD) is crucial, with electroencephalography (EEG) offering significant potential. However, developing accurate automated tools requires ...
This scientific investigation explored how meditation influences neural sound stimulus responses by employing EEG techniques during both meditative states and auditory oddball tasks. The study ...
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