What is developed where in the brain
Report for source localization module called “MUSIC”

The MUSIC module is designed based on combination of TRAP-MUSIC[1] and minimum norm algorithms. It takes the ERP of a certain condition as input and results in 1) the location of related sources, 2) the amplitude of every source, 3) the network, lobe and Brodmann’s area (BA) corresponding to the resultant sources and 4) a video clip of the sources’ fluctuations over the time. When the module is called, the user first selects a time point from the ERP of a certain condition. An interface is consequently popped up to let the user select the most relevant sources s/he would like to print in the report. To help the user investigate the nature of the computed sources, the module offers: demonstration of sources’ temporal information (changes over time), the location of sources on the template human brain MRI together with networks, lobes and BAs overlays on the MRI slices and an optional video clip to save in a directory for further exploration of overall sources’ activities together.

Maryam Rostami, PhD candidate, GTSG-Co Worker

MNI ICBM152 is utilized as the brain template to make the scanning grid for the source localization algorithm and also show the sources on its slices. The BAs’ labels are taken from a brain Atlas including 41 areas (the most important ones from BA1 to BA55). The networks Atlas is comprised of 10 distinct areas including somato-motor, cingular-opercular, auditory, default mode, visual, frontal-parietal, salience, subcortical, ventral-attention and dorsal-attention. The lobes Atlas covers 10 separate areas including prefrontal, motor strip, insula, parietal, temporal, occipital, limbic, cerebellum, subcortical and brainstem.

Given that in clinical recordings only 19 EEG electrodes exist and regarding the assumptions behind TRAP-MUSIC, the maximum number of sources can be 19. However, the number of sources is usually less than 11; thus, we have limited our graphical interface to include up to 11 sources in the same figure and leave the rest of them as unlikely sources. It is noteworthy that, the MUSIC module offers simple, user-friendly and semi-automatic interfaces to ensure that the user has insight over the behavior of sources. The informative pictures are designed providing the user with diverse information in such a way that can simplify the selection of sources to be saved in the report. In addition, the option of automatic sources’ selection results in the selection of most important sources in terms of the largest amplitude at the user-selected time point from the first ERP interface in a fully automatic way.  

MUSIC module can provide complementary information for a target ERP condition in terms of the position of the underlying sources and the contribution of each source in developing the ERP’s activity. Moreover, the Atlas labels (networks, lobes and BAs) beside the pictures provide a better insight of the function of the sources in addition to the place of them.

[1] Truncated recursively applied and projected multiple signal classification


annual report 2019
read and download

Arousal-marker for inner restlessness

Biomarkers and diagnostics
Important is the certainty and clarity of the professional

Arousal a marker for inner restlessness
Arousal modulates thoughts, behaviour and feelings

Arousal a marker for inner restlessness
Generalized arousal as a measure modulating behavior, emotions and cognitions

Good treatment

From stew to gender-specific personalised medicine
HBImed database, new version enables gender-specific diagnosis and treatment

HBImed database version 3.0 web-based
The world's largest database for evoked potentials now even better!

Biomarkers in diagnostics and treatment
Precise medical diagnostic of functions leads to a better understanding of human beeing

Biomarker/Neuromarker in Children with ADHD
Biomarkers/Neuroalgorithms as an additional Tool in clinical ADHD-Diagnoses

Depression and Arousal
The comparison of the Arousal Index (AI) with VIGALL as a diagnostic tool in depression patients and healthy controls during resting-state EEG