Single-Trial EEG Analysis Predicts Memory Retrieval and Reveals Source-Dependent Differences

We used pattern classifiers to extract features related to recognition memory retrieval from the temporal information in single-trial electroencephalography (EEG) data during attempted memory retrieval. Two-class classification was conducted on correctly remembered trials with accurate context (or s...

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Main Authors: Eunho Noh, Kueida Liao, Matthew V. Mollison, Tim Curran, Virginia R. de Sa
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-07-01
Series:Frontiers in Human Neuroscience
Subjects:
EEG
Online Access:https://www.frontiersin.org/article/10.3389/fnhum.2018.00258/full
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spelling doaj-f010364f41594557af831b2065a7f57e2020-11-25T02:19:38ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612018-07-011210.3389/fnhum.2018.00258310151Single-Trial EEG Analysis Predicts Memory Retrieval and Reveals Source-Dependent DifferencesEunho Noh0Kueida Liao1Matthew V. Mollison2Tim Curran3Virginia R. de Sa4Department of Electrical and Computer Engineering, University of California, San Diego, San Diego, CA, United StatesDepartment of Electrical and Computer Engineering, University of California, San Diego, San Diego, CA, United StatesDepartment of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, United StatesDepartment of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, United StatesDepartment of Cognitive Science, University of California, San Diego, San Diego, CA, United StatesWe used pattern classifiers to extract features related to recognition memory retrieval from the temporal information in single-trial electroencephalography (EEG) data during attempted memory retrieval. Two-class classification was conducted on correctly remembered trials with accurate context (or source) judgments vs. correctly rejected trials. The average accuracy for datasets recorded in a single session was 61% while the average accuracy for datasets recorded in two separate sessions was 56%. To further understand the basis of the classifier’s performance, two other pattern classifiers were trained on different pairs of behavioral conditions. The first of these was designed to use information related to remembering the item and the second to use information related to remembering the contextual information (or source) about the item. Mollison and Curran (2012) had earlier shown that subjects’ familiarity judgments contributed to improved memory of spatial contextual information but not of extrinsic associated color information. These behavioral results were similarly reflected in the event-related potential (ERP) known as the FN400 (an early frontal effect relating to familiarity) which revealed differences between correct and incorrect context memories in the spatial but not color conditions. In our analyses we show that a classifier designed to distinguish between correct and incorrect context memories, more strongly involves early activity (400–500 ms) over the frontal channels for the location distinctions, than for the extrinsic color associations. In contrast, the classifier designed to classify memory for the item (without memory for the context), had more frontal channel involvement for the color associated experiments than for the spatial experiments. Taken together these results argue that location may be bound more tightly with the item than an extrinsic color association. The multivariate classification approach also showed that trial-by-trial variation in EEG corresponding to these ERP components were predictive of subjects’ behavioral responses. Additionally, the multivariate classification approach enabled analysis of error conditions that did not have sufficient trials for standard ERP analyses. These results suggested that false alarms were primarily attributable to item memory (as opposed to memory of associated context), as commonly predicted, but with little previous corroborating EEG evidence.https://www.frontiersin.org/article/10.3389/fnhum.2018.00258/fullEEGmemory retrievalold/new effectmulti-variate analysisprediction
collection DOAJ
language English
format Article
sources DOAJ
author Eunho Noh
Kueida Liao
Matthew V. Mollison
Tim Curran
Virginia R. de Sa
spellingShingle Eunho Noh
Kueida Liao
Matthew V. Mollison
Tim Curran
Virginia R. de Sa
Single-Trial EEG Analysis Predicts Memory Retrieval and Reveals Source-Dependent Differences
Frontiers in Human Neuroscience
EEG
memory retrieval
old/new effect
multi-variate analysis
prediction
author_facet Eunho Noh
Kueida Liao
Matthew V. Mollison
Tim Curran
Virginia R. de Sa
author_sort Eunho Noh
title Single-Trial EEG Analysis Predicts Memory Retrieval and Reveals Source-Dependent Differences
title_short Single-Trial EEG Analysis Predicts Memory Retrieval and Reveals Source-Dependent Differences
title_full Single-Trial EEG Analysis Predicts Memory Retrieval and Reveals Source-Dependent Differences
title_fullStr Single-Trial EEG Analysis Predicts Memory Retrieval and Reveals Source-Dependent Differences
title_full_unstemmed Single-Trial EEG Analysis Predicts Memory Retrieval and Reveals Source-Dependent Differences
title_sort single-trial eeg analysis predicts memory retrieval and reveals source-dependent differences
publisher Frontiers Media S.A.
series Frontiers in Human Neuroscience
issn 1662-5161
publishDate 2018-07-01
description We used pattern classifiers to extract features related to recognition memory retrieval from the temporal information in single-trial electroencephalography (EEG) data during attempted memory retrieval. Two-class classification was conducted on correctly remembered trials with accurate context (or source) judgments vs. correctly rejected trials. The average accuracy for datasets recorded in a single session was 61% while the average accuracy for datasets recorded in two separate sessions was 56%. To further understand the basis of the classifier’s performance, two other pattern classifiers were trained on different pairs of behavioral conditions. The first of these was designed to use information related to remembering the item and the second to use information related to remembering the contextual information (or source) about the item. Mollison and Curran (2012) had earlier shown that subjects’ familiarity judgments contributed to improved memory of spatial contextual information but not of extrinsic associated color information. These behavioral results were similarly reflected in the event-related potential (ERP) known as the FN400 (an early frontal effect relating to familiarity) which revealed differences between correct and incorrect context memories in the spatial but not color conditions. In our analyses we show that a classifier designed to distinguish between correct and incorrect context memories, more strongly involves early activity (400–500 ms) over the frontal channels for the location distinctions, than for the extrinsic color associations. In contrast, the classifier designed to classify memory for the item (without memory for the context), had more frontal channel involvement for the color associated experiments than for the spatial experiments. Taken together these results argue that location may be bound more tightly with the item than an extrinsic color association. The multivariate classification approach also showed that trial-by-trial variation in EEG corresponding to these ERP components were predictive of subjects’ behavioral responses. Additionally, the multivariate classification approach enabled analysis of error conditions that did not have sufficient trials for standard ERP analyses. These results suggested that false alarms were primarily attributable to item memory (as opposed to memory of associated context), as commonly predicted, but with little previous corroborating EEG evidence.
topic EEG
memory retrieval
old/new effect
multi-variate analysis
prediction
url https://www.frontiersin.org/article/10.3389/fnhum.2018.00258/full
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