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Abstract
BackgroundWith 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 determined a priori. The utility of machine learning was investigated as a computational framework for extracting the most relevant features from EEG data empirically.MethodsSchizophrenia (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 5 frequency components (theta1, theta2, alpha, beta, gamma) at 4 processing 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.ResultsSWMT 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.ConclusionsEEG 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.
Highlights
With millisecond-level resolution, electroencephalographic (EEG) recording provides a sensitive tool to assay neural dynamics of human cognition
As a group, working memory performance was impaired in SZ participants based on MATRICS Consensus Cognitive Battery (MCCB) WM composite score, but visual attention was within normal range based on the Continuous Performance Test–Identical pairs (CPT-IP)
The current study employed machine learning classification to identify (1) EEG features predictive of Sternberg Working Memory Task (SWMT) accuracy in healthy adults, (2) EEG features predictive of SWMT accuracy in schizophrenia, and (3) controlling for SWMT accuracy, EEG features that distinguished healthy from schizophrenia group status
Summary
With millisecond-level resolution, electroencephalographic (EEG) recording provides a sensitive tool to assay neural dynamics of human cognition. Given that research-grade EEG systems are capable of sampling at 1000 samples per second and higher, a simple 10-min experiment could feasibly produce 600,000 discrete data points per channel of acquired data even before considering spatial characteristics or signal extraction methods (e.g., amplitude, spectral power, coherence) that further add to possible number of variables produced. Analysis of such data requires many decisions about the time points and signal extraction methods used to best characterize the psychophysiological phenomena under investigation. While this approach may provide important replication of prior results and incrementally advance knowledge, it may limit EEG analyses to a relatively small portion of the data collected, overlook important features of data not previously discovered, and constrains science to a confirmatory and deductive, rather than data inductive, position
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