A single-trial analytic framework for EEG analysis and its application to target detection and classification

Modern neuroimaging technologies afford a non-invasive view into the functions of the human brain with great spatial (fMRI) and temporal resolution (EEG). However, common signal analytic methods require averaging over many trials, which limits the potential for practical application of these technologies. In this paper we advance a novel single-trial analysis method for EEG and demonstrate this approach with a target detection task. The method utilizes a framework consisting of multiple processing modules that can be applied in whole or in part, including noise mitigation, source-space transformation, discriminant analysis, and performance evaluation. The framework introduces an enhanced noise mitigation technology based on Directed Components Analysis (DCA) that improves upon existing spatial filtering techniques. Source-space transformation, utilizing a finite difference model (FDM) of the human head, estimates activity measures of the cortical sources involved in task performance. Such a source-space discrimination provides measurement invariance between training and testing sessions and holds the promise of providing a degree of classification not possible with scalp-recorded EEG. The framework's discrimination modules interface with performance evaluation modules to generate classification performance statistics. When applied to EEG acquired during performance of a target detection task, this method demonstrated that neural signatures of target recognition correctly classified up to 87% of targets in a rapid serial visual presentation (RSVP) of target/non-target images. On average, the single-trial classification method resulted in greater than 60% improvement over behavioral performance for target detection.