A multi-task learning approach for the extraction of single-trial evoked potentials

Authors:
Costanza D'Avanzo;Anahita Goljahani;Gianluigi Pillonetto;Giuseppe De Nicolao;Giovanni Sparacino
Affiliations:
Department of Information Engineering, University of Padova, Via Gradenigo 6/B, 35131 Padova, Italy;Department of Information Engineering, University of Padova, Via Gradenigo 6/B, 35131 Padova, Italy;Department of Information Engineering, University of Padova, Via Gradenigo 6/B, 35131 Padova, Italy;Department of System Engineering and Computer Science, University of Pavia, via Ferrata 1, 27100 Pavia, Italy;Department of Information Engineering, University of Padova, Via Gradenigo 6/B, 35131 Padova, Italy
Venue:
Computer Methods and Programs in Biomedicine
Year:
2013

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Abstract

Evoked potentials (EPs) are of great interest in neuroscience, but their measurement is difficult as they are embedded in background spontaneous electroencephalographic (EEG) activity which has a much larger amplitude. The widely used averaging technique requires the delivery of a large number of identical stimuli and yields only an ''average'' EP which does not allow the investigation of the possible variability of single-trial EPs. In the present paper, we propose the use of a multi-task learning method (MTL) for the simultaneous extraction of both the average and the N single-trial EPs from N recorded sweeps. The technique is developed within a Bayesian estimation framework and uses flexible stochastic models to describe the average response and the N shifts between the single-trial EPs and this average. Differently from other single-trial estimation approaches proposed in the literature, MTL can provide estimates of both the average and the N single-trial EPs in a single stage. In the present paper, MTL is successfully assessed on both synthetic (100 simulated recording sessions with N=20 sweeps) and real data (11 subjects with N=20 sweeps) relative to a cognitive task carried out for the investigation of the P300 component of the EP.