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Table 7 Multiple Linear Regression Model Predicting SWMT Performance by SVM Model 1 Features

From: Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults

Step

R

R2

Std. Error

Δ R2

Δ F

Δ F p value

1

.450a

.202

9.740

.202

12.661

.001

2

.655b

.428

8.327

.226

19.410

.000

3

.832c

.692

6.177

.263

41.051

.000

4

.873d

.762

5.484

.070

13.894

.001

  1. Multiple linear regression based on all SVM Model 1 features achieved maximum fit based on frontal gamma during encoding and central theta 1 during retention. Features selected using forward-stepwise entry in the following order: 1 Central Retain Theta 1 – correct; 2 (1) + Frontal Encode Gamma – correct; 3 (1, 2) + Frontal Encode Gamma – incorrect, 4 Predictors (1, 2, 3) + Central Retain Theta 1 – incorrect