Over the past 20 years the use of QEEG brain mapping has been used to develop neurofeedback treatment strategies. The typical acquisition of the EEG involves eyes open and closed recordings where the subject is not engaged in any activity. Sixty seconds of artifact free data is selected and quantified into specific frequency bands and displayed on surface head maps. The data is also analyzed in 3D LORETA solutions so as to determine the spatial location of the EEG source.
The combination of these methods is typically used to generate neurofeedback protocols and has been shown to be effective in regulating the brain to a limited degree.
The evolution of computational neuroscience methods has added the use of machine learning methods that can generate independent components that have very high temporal resolution and source location capabilities that better identify actual neural activity. These methods have been applied to event related potential GO NOGO paradigms that record EEG when the subject is engaged in an active task versus the standard idling tasks typically recorded. The data obtained in ERP testing can show reaction time, neurological latency and desynchronization or absence of cross frequency coupling across electrode sites. This data, combined with the typical eyes open and closed EEG is sensitive enough to reveal specific deregulations that are highly correlated to behavioral and neurological conditions. This advanced form of analysis can better guide the clinician in developing treatment plans for the use of neurotherapy techniques.
In this presentation Dr. Dogris will show how advanced computational neuroscience methods involving ERP and QEEG data was utilized to develop specific treatment plans. Dr. Dogris will also discuss his evolving hypothesis regarding the combined use of random noise and tACS neurostimulation methods. Dr. Dogris will show ERP and QEEG data that demonstrates the impact of random noise (pink and brown), tACS and pEMF neurostimulation techniques.
Presented by: Nicholas Dogris