Comparison between Human and Artificial Neural Network Detection of Laplacian-Derived Electroencephalographic Activity Related to Unilateral Voluntary Movements

Abstract

A back-propagation artificial neural network (ANN) was tested to verify its capacity to select different classes of single trials (STs) based on the spatial information content of electroencephalographic activity related to voluntary unilateral finger movements. The rationale was that ipsilateral and contralateral primary sensorimotor cortex can be involved in a nonstationary way in the control of unilateral voluntary movements. The movement-related potentials were surface Laplacian-transformed (SL) to reduce head volume conductor effects and to model the response of the primary sensorimotor cortex. The ANN sampled the SL from four or two central channels overlying the primary motor area of both sides in the period of 80 ms preceding the electromyographic response onset in the active muscle. The performance of the ANN was evaluated statistically by calculating the percentage value of agreement between the STs classified by the ANN and those of two investigators (used as a reference). The results showed that both investigator and ANN were capable of selecting STs with the SL maximum in the central area contralateral to the movement (contralateral STs, about 25%), STs with considerable SL values also in the ipsilateral central area (bilateral STs, about 50%), and STs with neither the contralateral nor bilateral pattern (“spatially incoherent” single trials; about 25%). The maximum agreement (64–84%) between the ANN and the investigator was obtained when the ANN used four spatial inputs (P < 0.0000001). Importantly, the common means of all single trials showed a weak or absent ipsilateral response. These results may suggest that a back-propagation ANN could select EEG single trials showing stationary and nonstationary responses of the primary sensorimotor cortex, based on the same spatial criteria as the experimenter.