A hidden Markov model reliably characterizes ketamine-induced spectral dynamics in macaque local field potentials and human electroencephalograms

MLA

Shubham Chamadia, et al. “A Hidden Markov Model Reliably Characterizes Ketamine-Induced Spectral Dynamics in Macaque Local Field Potentials and Human Electroencephalograms.” PLOS Computational Biology, vol. 17, Aug. 2021, p. e1009280. EBSCOhost, https://doi.org/10.1371/journal.pcbi.1009280.

APA

Shubham Chamadia, Indie C. Garwood, Oluwaseun Akeju, Sourish Chakravarty, Pegah Kahali, Emery N. Brown, Earl K. Miller, Jacob A. Donoghue, & Meredith Mahnke. (2021). A hidden Markov model reliably characterizes ketamine-induced spectral dynamics in macaque local field potentials and human electroencephalograms. PLOS Computational Biology, 17, e1009280. https://doi.org/10.1371/journal.pcbi.1009280

Chicago

Shubham Chamadia, Indie C. Garwood, Oluwaseun Akeju, Sourish Chakravarty, Pegah Kahali, Emery N. Brown, Earl K. Miller, Jacob A. Donoghue, and Meredith Mahnke. 2021. “A Hidden Markov Model Reliably Characterizes Ketamine-Induced Spectral Dynamics in Macaque Local Field Potentials and Human Electroencephalograms.” PLOS Computational Biology 17 (August): e1009280. doi:10.1371/journal.pcbi.1009280.