Predicting Spiking Activity from Scalp EEG
This study predicts spiking activity from scalp EEG by analyzing spectrotemporal features, showing that combining phase, amplitude, and coupling improves predictions compared to single features.
Author
Rutgers, The State University of New JerseyORCID 0000-0003-2024-5892Unclaimed ORCID profile · follow to get notified of new preprints
3 papers
Recent·Most cited
This study predicts spiking activity from scalp EEG by analyzing spectrotemporal features, showing that combining phase, amplitude, and coupling improves predictions compared to single features.
Dixit Sharma, Shira M. Lupkin, Vincent B. McGinty
This study compares high-gamma LFPs and spiking activity in the OFC during value-based decisions, finding dissociable value and decision coding properties between the two signals.
John Thomas, Dixit Sharma, Sounak Mohanta, Neeraj Jain
The paper identifies distinct functional networks in the somatosensory area 3b of macaques and humans, showing different connectivity patterns for face and hand representations.