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Assessment of Vigilance Using EEG Source Localization

Salma K. Zeid1*
  • 1 American University of Sharjah, United Arab Emirates

I. INTRODUCTION Vigilance is a term that is mostly used as sustained attention. Researchers have found that people who participated in sustained attention type tasks suffered a noticeable decrease in their ability to detect critical stimuli over time. This phenomenon is known as vigilance decrement. The mindlessness, also known as underload, theory of vigilance decrement suggests that the monitor loses focus of attention due to the long intervals separating infrequent critical signals, where they start treating their vigilance assignment in a spontaneous automatic manner [1]. Vigilance is important for jobs where sustained attention for prolonged times is required. These jobs include air traffic control, luggage inspection, surveillance jobs and driving. Vigilance decrement can cause catastrophic consequences. Therefore, vigilance level assessment is a widely-researched topic. Several methods have been used to assess vigilance level such as eye tracking, heart rate variability, and physiological data such as electrocardiogram (ECG) and electroencephalogram (EEG). EEG is a noninvasive neuroimaging technique that measures scalp potentials due to firing of neurons in response to different stimuli. EEG data has been tightly correlated to vigilance. EEG has high temporal resolution of about a few milliseconds which makes it appropriate for real-time monitoring of brain activity [2]. The most studied characteristic of EEG signals in accordance with alertness level is power spectral density (PSD) of different brain waves: delta, theta, alpha, and beta. Event-related potential (ERP) is a useful EEG characteristics, which involves averaging the EEG activity time-locked to the presentation of a stimulus. Analysis of ERPs can aid in studying brain activity with respect to certain evoked stimuli. Functional descriptions of the ERPs components can identify the cognitive processes. The P300 is an ERP component which arises where a target stimulus is presented infrequently among more common non-target stimuli. The subject must pay attention and respond for a P300 to be elicited [3]. One factor that affects the P300 amplitude is attention, making it attractive to studies of populations with attention deficits. II. OBJECTIVES AND GOALS Vigilance decrement can cause fatalities in several critical tasks. Early detection of this decrement can allow reducing appalling costs. Therefore, the objective of this work is the construction of a vigilance assessment model using a novel method; EEG source localization. Additionally, the conventional PSD approach is used to compare with existing findings. The main challenge in this ongoing effort is developing an accurate vigilance measuring system that can detect vigilance decrement in critical conditions by monitoring neural source dynamics in real-time while ensuring high temporal and spatial resolutions. III. METHODOLOGY AND OUTLINE In this study, Advanced Source Analysis (ASA-lab) software is used to collect and analyze EEG data as well as reaction times from 30 subjects while performing a 20-minute task that induces vigilance decrement under controlled conditions. The task involves the subject reacting to target events among other non-target events. The recorded raw EEG data are preprocessed to remove unwanted signals to get a clean EEG signal showing only the subject’s brain activity. Furthermore, the signal is filtered to keep only frequency components of interest which are typically 0.1-30 Hz. Using ASA-lab two methods are used to study the brain behavior for low and high vigilance states. In the first method, the PSD of the EEG data is investigated and compared between low and high vigilance states. Second, sLORETA source localization algorithm is used to localize the P300 component to examine the changing aspects of the neural sources from low and high vigilance states. IV. RESULTS AND CONCLUSIONS The EEG signals is analyzed by comparing the first and last 5 minutes of the data which are regarded as awake and drowsy states respectively. The reaction time of subjects’ response to target stimuli is shown to increase as the task progresses as seen in figure 1 In PSD approach, the spectral behavior of brainwaves is compared between awake and drowsy states across different regions of the brain. The results summarized in table I show the trend among subjects in transition from awake to drowsy states. An increase in delta δ and alpha α waves is observed which could be due to their dominance in relaxed states. The increased theta θ and decreased beta β activity in the right and left temporal lobes were anticipated due to the association of beta waves with alertness and θ with drowsiness. The frontal theta power decrease agrees with [4]. In the second method, the P300 component was localized for awake and drowsy states using sLORETA algorithm. The source density distribution across the head is compared for both states to construct a preliminary vigilance assessment model. The most decrease was found to be in the parietal lobe. In [5], patients with parietal lesions as a group showed a significant impairment in processing speed which could explain the results obtained. Further studies on source dynamics of the brain with vigilance can promise a high precision vigilance measurement model in real-time applications.

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Acknowledgements

All work presented in the abstract would not have been possible without the constant support and supervision of my advisers Dr. Hasan Al-Nashash and Dr. Hasan Mir.

References

[1] W. S. Helton and P. N. Russell, “Feature absence–presence and two theories of lapses of sustained attention,” Psychological Research, vol. 75, no. 5, pp. 384–392, 2010.
[2] N. Cao, I. Yetik, A. Nehorai, C. Muravchik, and J. Haueisen, “Estimating Parametric Line-Source Models With Electroencephalography,” IEEE Transactions on Biomedical
[3] A. P. F. Key, G. O. Dove, and M. J. Maguire, “Linking Brainwaves to the Brain: An ERP Primer,” Developmental Neuropsychology, vol. 27, no. 2, pp. 183–215, 2005.
[4] I. P. Bodala, J. Li, N. V. Thakor, H. Al-Nashash, "EEG and Eye Tracking Demonstrate Vigilance Enhancement with Challenge Integration," Frontiers In Human Neuroscience, June, 2016. [Online]. Available: WorldCat, https://www.worldcat.org [Accessed: Dec. 6, 2016].
[5] P. V. Peers, C.J.H. Ludwig, C. Rorden, R. Cusack, C. Bonfiglioli, C. Bundesen, J. Driver, N. Antoun and J. Duncan, “Attentional functions of parietal and frontal cortex,” Cerebral Cortex, vol. 15, pp. 1469- 1484, 20

Keywords: Electroencephalogram, EEG source localization, Brainwaves, power spectral density, Event-Related Potentials, P300

Conference: 2nd International Conference on Educational Neuroscience, Abu Dhabi, United Arab Emirates, 5 Mar - 6 Mar, 2017.

Presentation Type: Poster Presentation

Topic: Educational Neuroscience

Citation: Zeid SK (2017). Assessment of Vigilance Using EEG Source Localization. Conference Abstract: 2nd International Conference on Educational Neuroscience. doi: 10.3389/conf.fnhum.2017.222.00025

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Received: 13 Feb 2017; Published Online: 11 Dec 2017.

* Correspondence: Ms. Salma K Zeid, American University of Sharjah, Sharjah, United Arab Emirates, salma.kzeid@gmail.com