Analyzing effect of distraction caused by dual-tasks on sharing of brain resources using SOM
DOI: 10.1109/IJCNN.2010.5596860 Conference: Neural Networks (IJCNN), The 2010 International Joint Conference on
Source: IEEE Xplore
Drivers' distraction is widely recognized as a leading cause of car accidents. To investigate the distracting effect of dual-tasks involving driving and answering mathematical equations in the stimulus onset asynchrony (SOA) conditions, we design five different cases: two cases involving single-tasks and three cases involving dual-tasks. We have found that there is no statistically significant change in the behavioral data among the three dual-tasks. This raises an important question - is there any detectable effect of the dual tasks on the brain waves? To answer this, we use the Self-Organizing Map (SOM) to recognize the changes, if any, in the Electroencephalography (EEG) dynamics associated with such dual-tasks. Our SOM analysis based on independent components corresponding to EEG signals extracted from Frontal and Motor areas revealed that single- and dual-tasks have distinguishable signatures in the EEG signals. Specifically, each of the two single-task conditions is clustered in a distinct spatial area of the map. Two of the dual-tasks also exhibit distinct spatial clusters, while the third case although shows differences from the other two, the neurons corresponding to this case are sub-clustered reflecting the fact that different subjects may give different priorities to the tasks when confronted with two tasks simultaneously. SOM-based exploratory analysis reveals the existence of distinct EEG signatures among the distracting and non-distracting tasks, although there is no any noticeable difference in the behavioral data among these cases.
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ABSTRACT: The self-organizing map, a neural network algorithm, was applied to the recognition of topographic patterns in clinical 22-channel EEG. Inputs to the map were extracted from short-time power spectra of all channels. Each location on a self-organized map entails a model for a cluster of similar input patterns; the best-matching model determines the location of a sample on the map. Thus, an instantaneous topographic EEG pattern corresponds to the location of the sample, and changes with time correspond to the trajectories of consecutive samples. EEG segments of "alpha," "alpha attenuation," "theta of drowsiness," "eye movements," "EMG artifact," and "electrode artifacts" were selected and labeled by visual inspection of the original records. The map locations of the labeled segments were different; the map thus distinguished between them. The locations of individual EEG's on the "alpha-area" of the map were also different. The clustering and easily understandable visualization of topographic EEG patterns are obtainable on a self-organized map in real time.IEEE Transactions on Biomedical Engineering 12/1995; 42(11):1062-8. DOI:10.1109/10.469372 · 2.35 Impact Factor
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ABSTRACT: Twenty subjects completed an on-the-road experiment that consisted of two parts on two separate days. One was a one-hour driving test under the influence of alcohol (BAC less than = 0.05%), the second a two-and-a-half-hour driving test under vigilance conditions. Impairment of driving performance was measured in a car-following test as well as in a standard driving test. Changes in relevant physiological parameters, such as ECG and EEG, reflected changes in driver status and predicted driving performance impairment.Accident Analysis & Prevention 09/1991; 23(4):297-307. DOI:10.1016/0001-4575(91)90007-R · 1.87 Impact Factor
Conference Paper: Distraction-related EEG dynamics in virtual reality driving simulation[Show abstract] [Hide abstract]
ABSTRACT: Driver distraction has been recognized as a significant cause of traffic incidents. Therefore, the aim of this study was to investigate electroencephalography (EEG) dynamics in response to distraction during driving. To study human cognition under specific driving task, we used virtual reality (VR) based driving simulation to simulate events including unexpected car deviations and mathematics questions (math) in real driving. For further assessing effects of the stimulus onset asynchrony (SOA) between the deviation onset and math presented on the EEG dynamics, we designed five cases with different SOA. The scalp-recorded EEG channel signals were first separated into independent brain sources by independent component analysis (ICA). Then, the event-related-spectral-perturbations (ERSP) measuring changes of EEG power spectra were used to evaluate the brain dynamics in time-frequency domains. Results showed that increases of theta band (5~7.8 Hz) and beta band (12.2~17 Hz) power were observed in the frontal cortex. Results demonstrated that reaction time and multiple cortical EEG sources responded to the driving deviations and math occurrences differentially in the stimulus onset asynchrony. Results also suggested that the theta band power increase in frontal area could be used as the distracted indexes for early detecting driver's inattention in the future.International Symposium on Circuits and Systems (ISCAS 2008), 18-21 May 2008, Sheraton Seattle Hotel, Seattle, Washington, USA; 05/2008
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