Poster Design & Printing by Genigraphics ® Leonard J. Trejo, Ph. D. Roman Rosipal, Ph. D Pacific Development and Technology, LLC Paul L. Nunez, Ph. D. Cognitive Dissonance, LLC We developed atomic decomposition algorithms for physiological estimation of cognitive status, including engagement, cognitive workload and mental fatigue. We validated the algorithms using EEG recordings from human participants who performed two different tasks: Task 1. Simulated control of UAVs (uninhabited aerial vehicles) Task 2. Continuous mental arithmetic for up to three hours. Each of two atomic decomposition algorithms identified EEG atoms with three dimensions: Algorithm 1. EEG power, EEG electrode position and time on task Algorithm 2. EEG coherence, EEG electrode pair and time on task. For the UAV control task we found atoms that combine sources in the theta (4-8 Hz) and alpha (8-12 Hz) EEG frequency bands consistently at different times of day and on different days. The temporal variations of the atoms clearly reflected the levels of mental workload induced by varying task conditions. For the mental arithmetic task we found atoms that tracked the development of mental fatigue in individual participants over time, while reflecting underlying changes in power or coherence of primarily theta-band EEG. By comparing these results with results of prior analyses using the same data sets, we find that atomic decomposition can supplement or overcome existing approaches based on conventional two- dimensional space-time or frequency- time decomposition of EEG. Web: ADVANCED PHYSIOLOGICAL ESTIMATION OF COGNITIVE STATUS As exemplified by Figures 3 and 4, we find that the PARAFAC models allow for robust estimation of mental workload. The results shown were replicated in two databases over 14 participants. As shown in Figures 5 and 6, a PARAFAC model provides for accurate estimation of mental fatigue. Other results (not shown) confirmed the model in 16 participants. We also find that inclusion of EEG coherence features in algorithms for estimating cognitive status improves on algorithms that rely on the EEG power spectrum alone. Neurocognitive Databases. We used three databases to develop and test the APECS algorithms: 1.USAF-C: C2ISR Multimodal Study of UAV Operator Readiness (n=6) 2.USA-T: Army Multimodal Study of Cognitive Overload (n=8) 3.NASA-C: NASA Cognitive Fatigue Database (n=16) For each database, we followed a three-step process of algorithm adaptation and testing (Figure 2). Prior knowledge of brain functional specialization guided our selection of EEG electrode sites, frequency bands, and spatial / temporal resolution for power and coherence. The temporal resolution of EEG analysis for each task was long enough to reliably estimate parameters but short enough to detect changes related to task conditions. By applying these principles to each task, we produced a set of analysis parameters (Table 1). For tasks that induced mental workload, we found PARAFAC atoms that combine sources in the theta and alpha EEG bands consistently in individual participants at different times of day and on different days. The temporal variations of the atoms clearly reflected the levels of mental workload induced by varying task conditions. For a task that induced mental fatigue, we found PARAFAC atoms that tracked the development of mental fatigue in individual participants over time, while reflecting underlying changes in power or coherence of primarily theta-band EEG. Our results show that APECS algorithms based on atomic decomposition are valuable new methods for the EEG- based estimation of cognitive status. By comparing our present results with results of prior analyses using the same data sets, we observed that atomic decomposition can supplement or overcome existing approaches based on conventional two-dimensional space-time or frequency-time decomposition of EEG. Over the past four years, we have developed advanced algorithms for real- time classification of mental states that can run on small hand-held computers or be embedded in the controls of vehicles, aircraft, spacecraft, and even in the helmets of foot soldiers, pilots, and astronauts. The algorithms use methods of machine learning and statistical pattern recognition, including kernel partial least squares (KPLS) and parallel factor analyses (PARAFAC). By applying these methods to massive amounts of experimental data we reduced multi-sensor EEG spectra to a small set of atoms that are important for estimating mental states. The algorithms can handle practically unlimited numbers of input channels and spectral resolutions, are adaptive, and require no a priori information of the spatial or spectral distributions of the atoms. Prior research suggests the hypothesis that cognitive workload is reflected by the desynchronization of a parietal alpha atom defined by long-range coherence with frontal regions and the synchronization of a frontal midline theta atom defined by local coherence with neighboring frontal regions. Mental fatigue, in contrast, is reflected by synchronization of both atoms. Our approach was to analyze multichannel EEG from each task and discover atoms reflecting these desynchronization and synchronization effects. For this study, we developed PARAFAC models of EEG power or coherence that allowed us to discover atoms in three dimensions, { e, f, t }. 1) Power: EEG power (f ) × EEG electrode (e ) × Time on task (t ) 2) Coherence: EEG coherence (f ) × EEG electrode-pair (e ) × Time on task (t ) Models were defined by the PARAFAC equation. Each atom is estimated by two normalized vectors (a,b), a score vector c and a noise term, ɛ eft. Graphical and conceptual illustrations of EEG atomic decomposition for the power model using PARAFAC are shown in Figure 1. INTRODUCTION METHODS AND MATERIALS DISCUSSION RESULTS ABSTRACT Table 1. EEG and Task Analysis Parameters Workload condtions (e.g., trials, time) Electrodes EEG Frequency Atoms Molecule with Covalent Bonds Basic EEG Sources (“atoms”) EEG atoms (“molecule”) with Coherence Bonds Figure 1. Using PARAFAC for EEG atomic decomposition Figure 5. PARAFAC power model during mental arithmetic task performance in one participant (G). Atom 1 (blue) reflected mostly wide-band noise from electrode F7. Atom 2 (red) reflected EEG power in alpha and theta bands. Figure 6. PARAFAC power model during first and last 15 minutes of a 3-hour mental arithmetic task performance in one participant (G). Atom 1 (blue) did not change over time. Atom 2 (red) reflected the development and progress of mental fatigue. Estimation of Mental Workload Estimation of Mental Fatigue Figure 3. PARAFAC power model during UAV task performance in one participant (3). The blue sections reflect periods of low workload (cruise); red sections reflect high workload (UAV control). Figure 4. PARAFAC coherence model during UAV task performance in one participant (3). The blue sections reflect periods of low workload (cruise); red sections reflect high workload (UAV control). Top 10% of inter-electrode coherences Magnitude squared coherence © Copyright 2010 Pacific Development and Technology, LLC Figure 2. Design and Analysis of APECS Algorithms