title: Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults.
abstract: BACKGROUND
  With millisecond-level resolution, electroencephalographic (EEG) recording provides a sensitive tool to assay neural dynamics of human cognition. However, selection of EEG features used to answer experimental questions is typically determineda priori. The utility of machine learning was investigated as a computational framework for extracting the most relevant features from EEG data empirically.
  
  
  METHODS
  Schizophrenia (SZ;n= 40) and healthy community (HC;n= 12) subjects completed a Sternberg Working Memory Task (SWMT) during EEG recording. EEG was analyzed to extract 5frequency components(theta1, theta2, alpha, beta, gamma) at 4processing stages(baseline, encoding, retention, retrieval) and 3 scalp sites (frontal-Fz, central-Cz, occipital-Oz) separately for correctly and incorrectly answered trials. The 1-norm support vector machine (SVM) method was used to build EEG classifiers of SWMT trial accuracy (correct vs. incorrect; Model 1) and diagnosis (HC vs. SZ; Model 2). External validity of SVM models was examined in relation to neuropsychological test performance and diagnostic classification using conventional regression-based analyses.
  
  
  RESULTS
  SWMT performance was significantly reduced in SZ (p< .001). Model 1 correctly classified trial accuracy at 84 % in HC, and at 74 % when cross-validated in SZ data. Frontal gamma at encoding and central theta at retention provided highest weightings, accounting for 76 % of variance in SWMT scores and 42 % variance in neuropsychological test performance across samples. Model 2 identified frontal theta at baseline and frontal alpha during retrieval as primary classifiers of diagnosis, providing 87 % classification accuracy as a discriminant function.
  
  
  CONCLUSIONS
  EEG features derived by SVM are consistent with literature reports of gamma's role in memory encoding, engagement of theta during memory retention, and elevated resting low-frequency activity in schizophrenia. Tests of model performance and cross-validation support the stability and generalizability of results, and utility of SVM as an analytic approach for EEG feature selection.
authors.affiliation: VA Connecticut Healthcare System, Psychology Service, 116-B, 950 Campbell Ave, West Haven, CT 06516, USA; Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
authors.forename: Jason K
authors.initials: JK
authors.lastname: Johannesen
authors.affiliation: Computer Science and Engineering, University of Connecticut, Storrs, CT, USA.
authors.forename: Jinbo
authors.initials: J
authors.lastname: Bi
authors.affiliation: Computer Science and Engineering, University of Connecticut, Storrs, CT, USA.
authors.forename: Ruhua
authors.initials: R
authors.lastname: Jiang
authors.affiliation: VA Connecticut Healthcare System, Psychology Service, 116-B, 950 Campbell Ave, West Haven, CT 06516, USA; Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
authors.forename: Joshua G
authors.initials: JG
authors.lastname: Kenney
authors.affiliation: Psychological Sciences, University of Connecticut, Storrs, CT, USA.
authors.forename: Chi-Ming A
authors.initials: CA
authors.lastname: Chen
keywords: EEG; Gamma frequency; Machine learning; Schizophrenia; Sternberg task; Support vector machine (SVM); Working memory