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Table 3 SVM Model 1 Coefficients Extracted by Stage of Working Memory

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

WM Stage

Frequency

Feature Weight

Baseline

 

Accuracy = .77

   Central

theta 2

−1.127

   Occipital

beta

−0.183

   Frontal

theta 1

−0.158

   Occipital

theta 2

−0.143

 

intercept

0.480

Encode

 

Accuracy = .96

   Frontal

gamma

−1.693

   Central

gamma

−0.748

 

intercept

0.607

Retain

 

Accuracy = .77

   Central

theta 1

−1.192

   Occipital

theta 2

−0.307

 

intercept

0.500

Retrieve

 

Accuracy = .88

   Occipital

gamma

−1.380

   Occipital

theta 2

−0.646

   Central

gamma

−0.261

 

intercept

0.597

  1. Four separate SVM Models were constructed in HN with entry of 60 EEG Features by WM Stage. Features extracted by 1-norm SVM to classify correct vs. incorrect trials with correct trials labeled 1 and incorrect labeled −1. Intercepts of four models were equivalent. All other features weighted at 0
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Neuropsychiatric Electrophysiology

ISSN: 2055-4788

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