A neural network predicting the amplitude of the N2pc in individual EEG datasets

Objective. The N2pc is a small amplitude transient interhemispheric voltage asymmetry used in cognitive neuroscience to investigate subject's allocation of selective visuo-spatial attention. N2pc is typically estimated by averaging the sweeps of the electroencephalographic (EEG) signal but, in absence of explicit normative indications, the number of sweeps is often based on arbitrariness or personal experience. With the final aim of reducing duration and cost of experimental protocols, here we developed a new approach to reliably predict N2pc amplitude from a minimal EEG dataset. Approach. First, features predictive of N2pc amplitude were identified in the time-frequency domain. Then, an artificial neural network (NN) was trained to predict N2pc mean amplitude at the individual level. By resorting to simulated data, accuracy of the NN was assessed by computing the mean squared error (MSE) and the amplitude discretization error (ADE) and compared to the standard time averaging (TA) technique. The NN was then tested against two real datasets consisting of 14 and 12 subjects, respectively. Main result. In simulated scenarios entailing different number of sweeps (between 10 and 100), the MSE obtained with the proposed method resulted, on average, 1/5 of that obtained with the TA technique. Implementation on real EEG datasets showed that N2pc amplitude could be reliably predicted with as few as 40 EEG sweeps per cell of the experimental design. Significance. The developed approach allows to reduce duration and cost of experiments involving the N2pc, for instance in studies investigating attention deficits in pathological subjects.