Single-trial detection in EEG and MEG: Keeping it linear.

Vision Technologies Laboratory, Sarnoff Corporation, Princeton, NJ 08540, USA; Department of Psychology and Department of Computer Science, University of New Mexico, Albuquerque, NM 87131, USA; Department of Psychology, Princeton University, Princeton, NJ 08544, USA; Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Biomedical Engineering, 351 Engineering Terrace, MC 8904, Columbia University, New York, NY 10027, USA
Neurocomputing 01/2003; 52-54:177-183. DOI: 10.1016/S0925-2312(02)00821-4
Source: DBLP

ABSTRACT Conventional electroencephalography (EEG) and magnetoencephalography (MEG) analysis often rely on averaging over multiple trials to extract statistically relevant differences between two or more experimental conditions. We demonstrate that by linearly integrating information over multiple spatially distributed sensors within a predefined time window, one can discriminate conditions on a trial-by-trial basis with high accuracy. We restrict ourselves to a linear integration as it allows the computation of a spatial distribution of the discriminating source activity. In the present set of experiments the resulting source activity distributions correspond to functional neuroanatomy consistent with the task (e.g. contralateral sensory-motor cortex and anterior cingulate).

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