Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Johannesen, Jason; Bi, Jinbo; Jiang, Ruhua; Kenney, Joshua G.; Chen, Chi-Ming A.
• Is Part Of: Neuropsychiatric electrophysiology. Volume 2:Issue 1(2016)
• Page Start: 1
• Page End: 21
• Publication Date: 2016-12
• Subjects: EEG -- Gamma frequency -- Support vector machine (SVM) -- Machine learning -- Sternberg task -- Working memory -- Schizophrenia; Neuropsychiatry -- Periodicals; Electrophysiology -- Periodicals; Brain -- Electric properties -- Periodicals; 616.807547
• DOI: 10.1186/s40810-016-0017-0
• Languages: English
• ISSNs: 2055-4788

Abstract

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.