Please note: This type of neurofeedback training has limited scientific evidence and at this stage is not considered an efficacious treatment for any specific disorders.
LORETA refers to low-resolution electromagnetic tomography and is similar to frequency training. EEG has been somewhat restricted to surface evaluations of the cortex and event related potentials (ERPs). Using EEG to provide data about source localisation of certain events in the brain has been a strong area of recent research. Dr Roberto Pascual-Marqui at the University Hospital of Psychiatry in Zurich is the developer of LORETA. LORETA is a collection of independent modules run in specific sequence in order to transform the raw EEG signal into LORETA images. It is one of the most extensively used algorithms for addressing the inverse solution for source localisation of the EEG produced on the scalp. LORETA and sLORETA (the standardised version) are able to estimate a direct 3D solution for electrical activity distribution that generates statical maps and models distribution currents of brain activity. It is able to plot these points on a standardised MRI atlas to then provide accurate estimations of anatomical labelling in subcortical structures such as the anterior cingulate, hippocampus, and amygdala. It has been demonstrated that LORETA is able to provide better temporal resolution than can be provided by PET or fMRI.
LORETA is based on a qEEG analysis where 19 or more sensors are usually used. The qEEG analysis is used to identify the underlying brain regions which are generating the brainwaves and need to be trained. Then, signals from all sensors are used to train these specific regions of the brain.
How does it work?
During sLORETA neurofeedback training, the physiological signal is correlated with a continuous feedback signal. The physiological signal is defined as the current density in a specified brain location calculated by algorithms of sLORETA. sLORETA is a widespread standardised linear, discrete, instantaneous, inverse solution proximity for brain electromagnetic measurements. The sLORETA algorithm estimates the current density in a three-dimensional space that results in the potential divergence on the scalp. sLORETA allows the continuous feedback to become a function of the intracranial current density and to co-vary with it.