Tracking Changes in Brain Network Connectivity under Transcranial Current Stimulation.

Noninvasive transcranial brain stimulation has been widely used in experimental and clinical applications to perturb the brain activity, aiming at promoting synaptic plasticity or enhancing functional connectivity within targeted brain regions. However, there are different types of neurostimulations and various choices of stimulation parameters; how these choices influence the intermediate neurophysiological effects and brain connectivity remain incompletely understood. We propose several quantitative methods to investigate the brain connectivity of an epileptic patient before and after transcranial alternating/direct current stimulation (tACS/tDCS).

A deep learning approach for real-time detection of sleep spindles.

Objective: Sleep spindles have been implicated in memory consolidation and synaptic plasticity during NREM sleep. Detection accuracy and latency in automatic spindle detection are critical for real-time applications.

Approach: Here we propose a novel deep learning strategy (SpindleNet) to detect sleep spindles based on a single EEG channel.