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.

A framework to support automated classification and labeling of brain electromagnetic patterns.

This paper describes a framework for automated classification and labeling of patterns in electroencephalographic (EEG) and magnetoencephalographic (MEG) data. We describe recent progress on four goals: 1) specification of rules and concepts that capture expert knowledge of event-related potentials (ERP) patterns in visual word recognition; 2) implementation of rules in an automated data processing and labeling stream; 3) data mining techniques that lead to refinement of rules; and 4) iterative steps towards system evaluation and optimization. This process combines top-down, or knowledge-driven, methods with bottom-up, or data-driven, methods.

Automated protocol for evaluation of electromagnetic component separation (APECS): Application of a framework for evaluating statistical methods of blink extraction from multichannel EEG.

Objective: We present APECS (Automated Protocol for Evaluation of Electromagnetic Component Separation), a framework for evaluating the accuracy of blind source separation algorithms in removing artifacts from EEG data. APECS applies multiple, automated procedures to quantify the extent to which blinks are removed, and the degree to which nonocular activity is left intact.

Methods: APECS was used to evaluate blink removal using three BSS algorithms: Second-Order Blind Inference (SOBI) and two Independent Component Analysis (ICA) implementations, FastICA and Infomax.