EEG-based local brain activity feedback training-tomographic neurofeedback

Abstract

Along with the development of distributed EEG source modeling methods, basic approaches to local brain activity (LBA-) neurofeedback (NF) have been suggested. Meanwhile several attempts using LORETA and sLORETA have been published. This article specifically reports on "EEG-based LBA-feedback training" developed by Bauer et al. (2011). Local brain activity-feedback has the advantage over other sLORETA-based approaches in the way that feedback is exclusively controlled by EEG-generating sources within a selected cortical region of training (ROT): feedback is suspended if there is no source. In this way the influence of sources in the vicinity of the ROT is excluded. First applications have yielded promising results: aiming to enhance activity in left hemispheric linguistic areas, five experimental subjects increased significantly the feedback rate whereas five controls receiving sham feedback did not, both after 13 training runs (U-test, p < 0.01). Preliminary results of another study that aims to document effects of LBA-feedback training of the Anterior Cingulate Cortex (ACC) and Dorso-Lateral Prefrontal Cortex (DLPFC) by fMRI revealed more local ACC-activity after successful training (Radke et al., 2014).

Keywords: EEG-based local brain activity (LBA-) feedback training; neurofeedback (NF); rtfMRI neurofeedback; sLORETA; tomographic neurofeedback (tNF).

Figure 1

Stronger BOLD-effect with the Age-Stroop after LBA-feedback training of the ACC [maximum at 6 24 −10; z = 7.77; FMRI-image thresholded at T = 4.86; MRT: 3T, TE = 28, TR = 2 s, 34 slices, 3.3 mm³ voxel size] adapted from Radke et al. (2014). The minor displacement of the BOLD-maximum from the ROT may result from slightly inaccurate individual ROT-localization—IHMs were already used, but no localizer EPs. Bottom right: averaged learning curve of 10 subjects.