3D_from_eeg

attempts to use resting/functional EEG to derive, via AI, a volume suitable for beamforming

big picture

To achieve acceptable source-level resolution while keeping high time resolution with EEG, individual MRI images are needed.
With this project I aim to test the feasibility of reconstructing a good-enough brain volume from EEG data alone.
The question is, whether there is enough information in hdEEG dynamics to allow for brain model reconstruction.

basic logic

• Sensors on surface pick up electrical information from source
• Aspects of the electr. signal is changing depending on source distance from sensor (SDS)
• SDS changes cyclically with heart beat
• SNR of these aspects (ASDS), to be determined via AI, is high enough when integrating data across the same positions within a cycle
• first ASDS candidate: neural noise

assumptions

• existence of ASDS, based on electrical measures, is not physically impossible
• heartbeat cycle detectable from EEG (can be tested)

roadmap

• given: ~20 EEG data, 60 electrodes, labelled (i.e. with individual MRIs)
• for each individual, 2 states (sober and alcohol-intoxicated :)
• first blind test with AI model
• ...for more, see issues