To investigate the brain dynamics related to distracted effects by using EEG signals, we assessed effects of the stimulus onset asynchrony (SOA) between the deviation and math presented on the EEG dynamics and we designed five cases with different SOA. This innovative study was conducted in a VR-environment on a 6 DOF motion platform. Our results showed that behavioral and physiological (EEG) responses under multiple cases and multiple distracted levels include: (1) Behavior:
the statistic test of response time to math in dual tasks was significantly larger than that in single task and the response time of single-math was shortest. This was because there was no another task to interference. However, comparing to the dual tasks, the response time to deviation was longest in the case of single-deviation. This was because the math task of the designed is difficult enough for the subjects and was considered as a real cue in the experiment. (2) Frontal component: (a) comparing to the single tasks, the phasic theta (5~7.8 Hz) band increase was higher in dual tasks.
The phasic changes around the theta band for the case, which the math presented at 400ms before the deviation onset, showed the strongest increases among all dual-task cases. (b) The latencies of the theta increase were shifted along with the onset of math presented. The latency for the case which the math presented at 400ms before deviation appeared was the shortest. (c) The Beta (12.2 ~ 17 Hz) increase was induced by the onsets of the math. (3) Motor component: alpha suppressions were time-locked to onsets of the first event. (4) Occipital component: (a) ERPs time-locked to the onsets of the math were showed in all cases. In comparison with the single task, the rebounded activities near the alpha band that induced by the button press were significantly decreased in the dual tasks.
When received a dual-task performance, subjects made math as a cue, and they could steer wheel rapidly. Comparing to among dual-task cases, the phasic theta band increases was higher in dual tasks. The phasic changes around the theta band for the case, which the math presented at 400ms before the deviation onset, showed the highest distracted effect in all cases. Because there was a processing task in brain first and subjects needed more brain source to manage the second task presented after the first task at 400 Ms. As for in the dual-task cases, less alpha suppression was in motor area, but more theta increase was in frontal area. These results demonstrated that reaction time and multiple cortical EEG sources responded to the car drifting and the math occurrences differentially in the stimulus onset asynchrony. In addition, results also suggested that the phasic theta increase in frontal area could be used as the index for early detecting driver’s distraction in the real driving.
In the future, firstly, we will apply our finding to take one step ahead to investigate the difference about spatial attention between motion and motionless on a 6 DOF motion platform. Secondary, in order to simulate real driving, we can investigate multi-sensory attention (such as auditory and visual). We will further investigate more detailed about the distracted effects of stimulus onset asynchrony. In the future, we use the study to combine the mechanism of bio-feedback and the bio-feedback provides a warning for the brain to adapt when subjects distract.
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Appendix
A. Independent Component Analysis (ICA)
The ICA is a statistical “latent variables” model with generative form:
)
Where A is a linear transform called a mixing matrix and the s are statistically i mutually independent. The ICA model describes how the observed data are generated by a process of mixing the components s . The independent components i s (often i abbreviated as ICs) are latent variables, meaning that they cannot be directly observed.
Also the mixing matrix A is assumed to be unknown. All we observed are the random variablesx , and we must estimate both the mixing matrix and the IC’s i s using i thex . i
x in N-dimension, ICA will find a linear mapping
W such that the unmixed signals u (t) is statically independent.
)
Supposed the probability density function of the observations x can be expressed as:
)
the learning algorithm can be derived using the maximum likelihood formulation with the log-likelihood function derived as:
∑
=Thus, an effective learning algorithm using natural gradient to maximize the
log-likelihood with respect to W gives:
Where the nonlinearity
T
and WTW rescales the gradient, simplifies the learning rule and speeds the convergence considerably. It is difficult to know a priori the parametric density function p u , which plays an essential role in the learning process. If we choose to ( ) approximate the estimated probability density function with an Edgeworth expansion or Gram-Charlier expansion for generalizing the learning rule to sources with either sub- or super-Gaussian distributions, the nonlinearity ϕ( u) can be derived as:
⎩⎨
Since there is no general definition for sub- and super-Gaussian sources, we choose
(
(1,1) (-1,1))
super-Gaussian, respectively, where N(
μ,σ2)
is a normal distribution. The learning rules differ in the sign before the tanh function and can be determined using a switching criterion as:[ ]
where
{ } { } { }
(
sec 2( i) i2 tanh( i) i)
,i =sign E h u E u −E u u
κ (10)
represents the elements of N-dimensional diagonal matrix K. After ICA training, we can obtain N ICA components u(t) decomposed from the measured N-channel EEG data x(t). In this study, N=30, thus we obtain 30 components from 30 channel signals.
).