Abstract: Electroencephalogram (EEG) signals to classify sleep stages have emerged as a vital area of research, aiming to provide non-invasive measures of people's neurological and cognitive states.
Zhao, W., Jiang, X., Zhang, B. et al. CTNet: a convolutional transformer network for EEG-based motor imagery classification. Sci Rep 14, 20237 (2024). https://doi.org ...
We enrolled 30 patients with MDD and 40 healthy controls. Resting-state EEG was recorded and analyzed using microstate segmentation (classes A–D). Temporal parameters (mean duration, occurrence, time ...
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