Decomposing EEG components of Memory Processing

Virginia will discuss work on single-trial analysis of EEG signals during episodic memory tasks. They develop single-trial classifiers to predict whether people will remember pictures from EEG recorded prior to and during study presentation. The single-trial classification analysis provides discriminative dimensionality reduction which can be used to project untrained data, including untrained classes, to reveal novel electrophysiological distinctions between behavioral states. They also analyze EEG signals recorded during the test phase and show that they can model all 13 different classes of trials from the combination of participants' objective memory performance (e.g. correct rejects, false alarms,...) as well as their perceived performance (e.g. sure new, maybe new ...) as a function of only three different basis classifiers. Together these results show how projecting untrained classes onto classifiers trained on other classes can help decompose EEG signals and reveal novel electrophysiological correlates of behavior.

Learning Objectives:

1. Review the basic terms and concepts in recognition memory research.

2. Summarize the basic limitations of EEG signals as well as some notable features that relate to memory processing.

3. Recognize the benefits of single trial classification for EEG analysis.