Exploring the EEG Correlates of Neurocognitive Lapse with Robust Principal Component Analysis Chun-Shu Wei 1,2,3 , Yuan-Pin Lin 1 , and Tzyy-Ping Jung 1,2,3(&) 1 Swartz Center for Computational Neuroscience, Institute for Neural Computation, Atlanta, USA {cswei,yplin,jung}@sccn.ucsd.edu 2 Institute of Engineering in Medicine, San Diego, USA 3 Department of Bioengineering, University of California San Diego, La Jolla, San Diego, CA, USA Abstract. Recent developments of brain-computer interfaces (BCIs) for driv- ing lapse detection based on electroencephalogram (EEG) have made much progress. This study aims to leverage these new developments and explore the use of robust principal component analysis (RPCA) to extract informative EEG features associated with neurocognitive lapses. Study results showed that the RPCA decomposition could separate lapse-related EEG dynamics from the task-irrelevant spontaneous background activity, leading to more robust neural correlates of neurocognitive lapse as compared to the original EEG signals. This study will shed light on the development of a robust lapse-detection BCI system in real-world environments. Keywords: EEG Á BCI Á RPCA Á Drowsiness Á Lapse Á Driving Á Fatigue 1 Introduction The neurocognitive lapse has been known as a critical safety issue in vehicle driving. Such momentary lapse causes approximate 1.9 million drivers to fatal car accidents with injury or death [1]. Technologies that enable instant lapse detection and feedback delivery to rectify drivers from the occurrence of lapse are thus urgently required. For the past two decades, the noninvasive brain-sensing technology, namely electroen- cephalogram (EEG), has been adopted for this purpose because of its high temporal resolution of brain signals allowing a prompt response to a neurocognitive lapse. For example, studies have shown strong EEG correlates of behavioral lapses, including power spectra [2–6] and autoregressive features [7, 8]. These EEG features could then be used to develop various on-line/off-line neuroergonomic systems for monitoring drowsiness, fatigue, and behavioral lapse in task performance [2, 3, 9–12]. It is believed that an effective computational approach that can further leverage EEG cor- relates of neurocognitive lapse is a crucial step for improving the practicability of BCI-based lapse detection system in real life, which is the main focus of this study. © Springer International Publishing Switzerland 2016 D.D. Schmorrow and C.M. Fidopiastis (Eds.): AC 2016, Part I, LNAI 9743, pp. 113–120, 2016. DOI: 10.1007/978-3-319-39955-3_11