Conference Paper

Analyzing effect of distraction caused by dual-tasks on sharing of brain resources using SOM

DOI: 10.1109/IJCNN.2010.5596860 In proceeding of: Neural Networks (IJCNN), The 2010 International Joint Conference on
Source: IEEE Xplore

ABSTRACT 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.

  • [Show abstract] [Hide abstract]
    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. · 2.35 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.
    Psychophysiology 02/2000; 37(2):163 - 178. · 3.29 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents the findings of a simulator study that examined the effects of distraction upon driving performance for drivers in three age groups. There were two in-vehicle distracter tasks: operating the vehicle entertainment system and conducting a simulated hands-free mobile phone conversation. The effect of visual clutter was examined by requiring participants to drive in simple and complex road environments. Overall measures of driving performance were collected, together with responses to roadway hazards and subjective measures of driver perceived workload. The two in-vehicle distraction tasks degraded overall driving performance, degraded responses to hazards and increased subjective workload. The performance decrements that occurred as a result of in-vehicle distraction were observed in both the simple and complex highway environments and for drivers in different age groups. One key difference was that older drivers traveled at lower mean speeds in the complex highway environment compared with younger drivers. The conclusions of the research are that both in-vehicle tasks impaired several aspects of driving performance, with the entertainment system distracter having the greatest negative impact on performance, and that these findings were relatively stable across different driver age groups and different environmental complexities.
    Accident Analysis & Prevention 02/2006; 38(1):185-91. · 1.87 Impact Factor