Monte-Carlo simulation to reduce sensor dimension of EEG neurofeedback device

Neuro-feedback (NF) training using EEG device is finding wide acceptance for treatment of ADHD, epilepsy, anxiety, dyslexia, schizophrenia etc. In realistic clinical practice, high quality delivery of NF signal is possible only with high sensor density devices.

Unfortunately, these are often cost-prohibitive, time consuming and unmanageable due to large number of sensors. So, reduction of sensor dimension without compromising the quality of the signal is an important clinical problem. On the contrary, inexpensive low density devices lacks clinical precision. This can be solved by generating reduced dimension sensor configuration by Monte Carlo (MC) sampling of high-quality data. In our experiment, high quality EEG data was collected from NF sessions with 72 subjects.

MC sampling of all possible 32 configurations were used to generate a targeted set of montages to produce NF source signal equivalent to those from the original high-density configuration. We found a large pool of potential montage configurations with only 32 sensors that can reproduce results from high density sensor system with more than 80%. Thus, MC sampling can be utilized to design low cost clinical grade EEG devices without compromising the quality.

The paper was published at: Proceedings of: 'APS Meeting Abstracts'.

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This work was supported (in part) by the Fetzer Franklin Fund of the John E. Fetzer Memorial Trust.