From Research to Clinic: A sensor reduction method for high-density EEG neurofeedback systems

Publication date: Available online 16 December 2018

Source: Clinical Neurophysiology

Author(s): Prasanta Pal, Daniel L. Theisen, Michael Datko, Remko van Lutterveld, Alexandra Roy, Andrea Ruf, Judson A. Brewer

Abstract

Objective

To accurately deliver a source-estimated neurofeedback (NF) signal developed on a 128-sensors EEG system on a reduced 32-sensors EEG system.

Methods

A linearly constrained minimum variance beamformer algorithm was used to select the 64 sensors which contributed most highly to the source signal. Monte Carlo-based sampling was then used to randomly generate a large set of reduced 32-sensors montages from the 64 beamformer-selected sensors. The reduced montages were then tested for their ability to reproduce the 128-sensors NF. The high-performing montages were then pooled and analyzed by a k-means clustering machine learning algorithm to produce an optimized reduced 32-sensors montage.

Results

Nearly 4,500 high-performing montages were discovered from the Monte Carlo sampling. After statistically analyzing this pool of high performing montages, a set of refined 32-sensors montages was generated that could reproduce the 128-sensors NF with greater than 80% accuracy for 72% of the test population.

Conclusion

Our Monte Carlo reduction method was used to create reliable reduced-sensors montages which could be used to deliver accurate NF in clinical settings.

Significance

A translational pathway is now available by which high-density EEG-based NF measures can be delivered using clinically accessible low-density EEG systems.

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