Advances in Electrical Neuroimaging, Brain Networks and Neurofeedback Protocols

Abstract

Human EEG biofeedback (neurofeedback) started in the 1940s using 1 EEG recording channel, then to 4 channels in the 1990s. New advancements in electrical neuroimaging expanded EEG biofeedback to 19 channels using Low Resolution Electromagnetic Tomography (LORETA) three-dimensional current sources of the EEG. In 2004–2006 the concept of a “real-time” comparison of the EEG to a healthy reference database was developed and tested using surface EEG z-score neurofeedback based on a statistical bell curve called “real-time” z-scores. The “real-time” or “live” normative reference database comparison was developed to help reduce the uncertainty of what threshold to select to activate a feedback signal and to unify all EEG measures to a single value, i.e., the distance from the mean of an age matched reference sample. In 2009 LORETA z-score neurofeedback further increased the specificity by targeting brain network hubs referred to as Brodmann areas. A symptom check list program to help link symptoms to dysregulation of brain networks based on fMRI and PET and neurology was created in 2009. The symptom checklist and NIH based networks linking symptoms to brain networks grew out of the human brain mapping program starting in 1990 which is continuing today. A goal is to increase specificity of EEG biofeedback by targeting brain network hubs and connections between hubs likely linked to the patient’s symptoms. New advancements in electrical neuroimaging introduced in 2017 provide increased resolution of three-dimensional source localization with 12,700 voxels using swLORETA with the capacity to conduct cerebellar neurofeedback and neurofeedback of subcortical brain hubs such as the thalamus, amygdala and habenula. Future applications of swLORETA z-score neurofeedback represents another example of the transfer of knowledge gained by the human brain mapping initiatives to further aid in helping people with cognition problems as well as balance problems and parkinsonism. A brief review of the past, present and future predictions of z-score neurofeedback are discussed with special emphasis on new developments that point toward a bright and enlightened future in the field of EEG biofeedback.

A Review of Neurofeedback Training for Improving Sport Performance From the Perspective of User Experience

“…A test of trainability includes verifying whether participants can change the feedback characteristics used in the SP–NFT. In SP–NFT, it is theoretically possible to use any EEG characteristics as feedback characteristics, such as enhancing the activity of a certain brain region, enhancing the connectivity between the two brain regions, and even activating the amygdala of the brain using LORETA technology ( Thatcher et al, 2020 ).…”

“…Compared with traditional single-brain region activation and inhibition training, this method pays more attention to the information interaction and overall coordination of each region. At present, some studies have gradually begun to study NFT based on functional connectivity and brain network characteristics and have obtained preliminary significant results ( Ramot et al, 2017 ; Thatcher et al, 2020 ). In the future, SP–NFT based on functional connectivity and brain networks may be an important training scheme.…”

Who's driving? The default mode network in healthy elderly individuals at risk of cognitive decline

“…Furthermore, it is important to emphasize that the average clinician will only have access to a 19-channel device. Several studies have looked into the accuracy of source localization with different numbers of channels and it appears that, when the signal-to-noise ratio is adequate, a 19-channel recording is fairly accurate ( 112 – 114 ), which of course has positive implications in clinical settings. Future research could be aimed at exploring cognitive differences between these two groups using more demanding tests, perhaps with an emphasis on frontal lobe functioning.…”

Z-score neurofeedback, heart rate variability biofeedback, and brain coaching for older adults with memory concerns

“…The z-score for a particular QEEG parameter represents the number of standard deviations from the age-normed mean, and participants are trained to approach z = 0. Although few high-quality studies currently exist for the use of z-score NFB (R. Coben et al, 2019), a number of published studies show promising results of z-score NFB for a variety of conditions (Thatcher et al, 2020), including ADHD (Groeneveld et al, 2019;, pain perception (Prinsloo et al, 2018), and cognitive dysfunction (Koberda, 2014). HRV refers to the variability in timing between heart beats (De Jong & Randall, 2005), and it is governed by both the sympathetic and parasympathetic nervous systems.…”