Objective: The ability to measure event-related potentials (ERPs) as practical, portable brain vital signs is limited by the physical locations of electrodes. Standard electrode locations embedded within the hair result in challenges to obtaining quality signals in a rapid manner. Moreover, these sites require electrode gel, which can be inconvenient. As electrical activity in the brain is spatially volume distributed, it should be possible to predict ERPs from distant sensor locations at easily accessible mastoid and forehead scalp regions.
Methods: An artificial neural network was trained on ERP signals recorded from below hairline electrode locations (Tp9, Tp10, Af7, Af8 referenced to Fp1, Fp2) to predict signals recorded at the ideal Cz location.
Results: The model resulted in mean improvements in intraclass correlation coefficient relative to control for all stimulus types (Standard Tones: +9.74%, Deviant Tones: +3.23%, Congruent Words: +15.25%, Incongruent Words: +25.43%) and decreases in RMS Error (Standard Tones: -26.72%, Deviant Tones: -17.80%, Congruent Words: -28.78%, Incongruent Words: -29.61%) compared to the individual distant channels. Measured vs predicted ERP amplitudes were highly and significantly correlated with control for the N100 (R = 0.5, padj < 0.05), P300 (R = 0.75, padj < 0.01), and N400 (R = 0.75, padj < 0.01) ERPs.
Conclusion: ERP waveforms at distant channels can be combined using a neural network autoencoder to model the control channel features with better precision than those at individual distant channels. This is the first demonstration of feasibility of predicting evoked potentials and brain vital signs using signals recorded from more distant, practical locations.
Significance: This solves a key engineering challenge for applications that require portability, comfort, and speed of measurement as design priorities for measurement of event-related potentials across a range of individuals, settings, and circumstances.