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Using discriminative dimensionality reduction to understand the neural basis of recognition memory encoding and retrieval /

Abstract

We present results from single-trial analyses conducted on Electroencephalography (EEG) data recorded during recognition memory experiments. Chapter 2 gives an overview on EEG analysis methods including various classification and feature extraction methods for EEG. The chapter also gives a review of previous findings on neural correlates of recognition memory. In Chapter 3, we propose a novel way to use discriminative classification analysis to project high-dimensional EEG data onto a low- dimensional discriminative space for visualization, analysis, and statistical testing. This multivariate analysis directly controls for the multiple comparison problem (MCP) by effectively reducing the number of test variables. A major advantage of this approach is that it is possible to compare the brain activity across different conditions even when the trial count is low, provided that a sufficient number of trials are used to establish the initial hyperplane(s), meaning that error conditions and conditions that divide subtle behavioral differences can be readily compared. Currently these data are either ignored or lumped with other data thereby losing the ability to reveal the neural mechanisms underlying subtle behavioral differences. The proposed method provides a powerful tool to analyze conditions with relatively small numbers of trials from high-dimensional neural recordings. In Chapter 4, we show that it is possible to successfully predict subsequent memory performance based on single- trial EEG activity before and during item presentation in the study phase. Two-class classification was conducted to predict subsequently remembered vs. forgotten trials based on subjects' responses in the recognition phase. The overall accuracy across 18 subjects was 59.6 \% by combining pre- and during-stimulus information. The single -trial classification analysis provides a dimensionality reduction method to project the high-dimensional EEG data onto a discriminative space. These projections revealed novel findings in the pre- and during-stimulus periods related to levels of encoding. It was observed that the pre-stimulus information (specifically oscillatory activity between 25 and 35 Hz) -300 to 0 ms before stimulus presentation and during-stimulus alpha (7-12 Hz) information between 1000 and 1400 ms after stimulus onset distinguished between recollection and familiarity while the during-stimulus alpha information and temporal information between 400 and 800 ms after stimulus onset mapped these two states to similar values. In Chapter 5, we show that it is possible to predict successfully identified old vs. new items based on single-trial EEG activity recorded during the retrieval phase of 4 separate datasets. Two-class classification was conducted on the trials with source (frame color/spatial location of the study item) correct trials with high confidence responses vs. correctly rejected trials. The average accuracy for the datasets recorded in a single session was 62.2 % while the average accuracy for the datasets recorded in two separate sessions was 58.7 %. The classifier outputs revealed novel findings related to retrieval strength from the EEG data. The classifier outputs from all 4 datasets reflected whether the subjects remembered the source information and also whether the subjects believed they remembered the source information. Furthermore, the source correct trials where the subjects believed they correctly remembered the source information were recognized as the highest retrieval strength condition by the classifiers. Cross-source classification analysis showed that the frontal old/new effect was affected by source type (location vs. color) possibly due to prefrontal and parahippocampal involvement in location retrieval whereas the parietal old/new effect was source type invariant

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