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Electroencephalography (EEG) is a method for monitoring electrical activity in the brain. It uses electrodes placed on or below the scalp to record activity with coarse spatial but high temporal resolution. EEG can be used in cognitive research or to diagnose conditions such as epilepsy and sleep disorders.
An EEG study reports sound-induced suppression of the earliest visual cortical activity in a rare group of humans who were born pattern vision blind but regained sight, indicating persistent crossmodal visual cortical activation after sight recovery.
The authors interrogate patterns of symptom-linked resting-state functional connectivity measured with electroencephalography with machine learning techniques to identify two dimensions associated with specific regions, the left angular gyrus and the right middle temporal gyrus and social and communication deficits, and the right inferior parietal lobe with restricted and repetitive disorders, which may serve as biomarkers in autism.
A fast and accurate time–frequency analysis is challenging for many applications, especially in the current big data era. A recent work introduces a fast continuous wavelet transform that effectively boosts the analysis speed without sacrificing the resolution of the result.