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Fig 1.

Electrode configuration for the EEG data collection.

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Table 1.

The distribution of classes across epochs and total epochs per participant.

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Table 2.

Participant demographics.

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Table 3.

Comparison of EEG and non-EEG participants on various measures.

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Fig 2.

Top Left: MW rate versus immediate recall during Lecture 1. Top Right: MW rate versus retention during Lecture 1. Bottom Left: MW rate versus immediate recall during Lecture 2. Bottom Right: MW rate versus retention during Lecture 2.

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Fig 3.

Power spectrum distributions for each channel in each frequency band, aggregated across participants.

Statistical significance is calculated using paired-samples t-tests. Uncorrected p-values are reported as follows: *, p < 0.05, **, p < 0.01, ***, p < 0.001, ****, p < 0.0001.

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Table 4.

Results of repeated-measures ANOVA models for each channel.

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Fig 4.

Power spectrum distributions for each participant in each frequency band, aggregated across channels.

Statistical significance is calculated using paired-samples t-tests. Uncorrected p-values are reported as follows: *, p < 0.05, **, p < 0.01, ***, p < 0.001, ****, p < 0.0001.

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Table 5.

Results of repeated-measures ANOVA models for each participant.

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Fig 5.

Classification performance for the Artifacts Suppressed approach given by the F1 score for each participant and frequency band.

The rightmost set of bars are the averages across participants. Standard error bars are given for five cross-validation runs for each participant and all 15 participants for the averaged accuracies. The 95% confidence intervals for chance-level F1 scores per participant are plotted as grey regions, and statistical significance is calculated using independent two-sample t-tests using the best frequency band. *: p < 0.01, **: p < 0.001, ***: p < 0.0001.

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Fig 6.

Classification performance for the Artifacts Present approach given by the F1 score for each participant and frequency band.

The rightmost set of bars are the averages across participants. Standard error bars are given for five cross-validation runs for each participant and all 15 participants for the averaged accuracies. The 95% confidence intervals for chance-level F1 scores per participant are plotted as grey regions, and statistical significance is calculated using independent two-sample t-tests using the best frequency band. *: p < 0.01, **: p < 0.001, ***: p < 0.0001.

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Fig 7.

The learned common spatial pattern for four selected participants within frequency band yielding optimal detection (due to space restrictions) after artifact rejection.

Note that these patterns do not reflect brain activations; rather, they show where the greatest change in activation took place between MW versus not MW. Pattern one refers to the learned pattern that is most strongly indicative of MW or not MW (listed as MW 1 or not MW 1), and increasing pattern number refers to less predictive patterns.

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Table 6.

Classification performance (with standard error across cross-validation runs) for the best frequency band using the common spatial pattern algorithm with the Artifacts Suppressed approach.

Frequency band chosen based on F1 score. Average performance (with standard deviation across participants) is given by the best frequency band for each participant. Observed MW rate reflects the MW rate of the processed epochs after epoch removal, and therefore may not be the exact rate of MW observed in the probe responses. Predicted MW rate is the rate at which the machine learning model predicts MW.

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Table 7.

Classification performance (with standard error across cross-validation runs) for the best frequency band using the common spatial pattern algorithm with the Artifacts Present approach.

Frequency band chosen based on F1 score. Average performance (with standard deviation across participants) is given by the best frequency band for each participant. Observed MW rate reflects the MW rate of the processed epochs after epoch removal, and therefore may not be the exact rate of MW observed in the probe responses. Predicted MW rate is the rate at which the machine learning model predicts MW.

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Fig 8.

The relationships between observed and predicted MW rates.

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