The aim of this study is to investigate Electroencephalography EEG -based brain dynamics in response to driving distraction. To study human cognition under specific driving tasks in a simulated driving experiment, this study utilized two simulated events including unexpected car deviations and mathematics questions. The raw data were first separated into independent brain sources by Independent Component Analysis. Then, the EEG power spectra were used to evaluate the time-frequency brain dynamics. Results showed that increases of theta band and beta band power were observed in the frontal cortex.
Further analysis demonstrated that reaction time and multiple cortical EEG power had high correlation. Thus, this study suggested that the features extracted by EEG signal processing, which were the theta power increases in frontal area, could be used as the distracted indexes for early detection of driver inattention in real driving. Article :. DOI: AWAKE for subject 4. See text for details.https://grupoavigase.com/includes/324/2458-servicio-de.php
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Awake state having higher IPI prediction error than REM state is somewhat unexpected, which we will discuss further in the Discussion section. A Subject 1, B subject 2, C subject 3, D subject 4. Figure 4. The x -axis is in linear scale while the y -axis is in log scale for a clearer view of the probability of extreme error values. The unit for the x -axis was 10 ms.
The trends are consistent for all four subjects. REM has the highest peak near zero error, closely followed by awake state, and finally SWS which shows the lowest peak. SWS has the heaviest tail, meaning that high error values are much more common than awake state or REM. All differences, except for REM vs. These results support our hypothesis regarding the predictability of internal state dynamics and conscious states i. For t -test, the absolute error in IPI prediction was log-transformed to correct for the positive skewness of the IPI error distributions Figure 4. SWS, between small and medium 0.
See Table 1 for details. A medium effect size is large enough to compare means without further statistical analysis, while a small effect size requires further analysis Cohen, The statistical power of t -test depends on three factors: the mean differences, the residual variance, and the sample size. Given a fixed Cohen's d , increasing sample size improves statistical power; since the degrees of freedom of the t -test are increased, the mean differences do not need to be as large to be significant Kenny, Based on this, we can assert the main interpretation above.
We also ran the Fast Fourier Transform FFT power spectral analysis with the EEG data to rule out the possibility that of our findings simply reflect the varying power of alpha waves in the three tested conditions. Alpha waves are in the frequency range of 7. Therefore, alpha waves are probably most predictable neural oscillations in EEG brain signals. In our FFT power spectral analysis results Figure 5 , alpha waves were not notably observed for all data, even in the awake states.
This is partly because alpha waves are reduced with open eyes, drowsiness, and sleep. Therefore, it seems that there is no strong association between IPI prediction and the alpha wave spectral power in the EEG data. Figure 5. Most peaks are observed near 1 Hz and 2 Hz. Note that the results shown here are based on the raw EEG data, not the IPI data, and that the y -axis are scaled differently to fit the data. There were a couple of interesting properties we observed in the results. This was somewhat unexpected since we hypothesized predictability will be correlated with the degree of consciousness and by default we expected that the awake state is the most conscious.
This is an interesting counterintuitive result. Second, all error distributions have a broader spread toward positive error, relative to negative error i. This could be due to the skewness in the IPI distribution itself Figure 6 : See the Discussion section for a detailed discussion on both phenomena. Figure 6. For all cases, the IPI distributions are positively skewed. The skewness varied from 0. In this article, we analyzed publicly available EEG data from sleep and awake states to measure the predictability of the signals under conscious awake and REM sleep and unconscious SWS conditions.
We found that the predictability of EEG signals correlated with the degree of consciousness. These results support our earlier hypothesis that predictable internal brain dynamics is a necessary condition of consciousness. In the following, we will discuss potential issues and interesting observations from our study, and propose potential applications of our finding to time perception and neurorobotics. There are potential limitations of our approach as we briefly mentioned in the Materials and Methods section.
This could be due to multiple factors, one of which is the nature of the EEG signals. For example, EEG signals are weighted mixtures of on-going electrical activity in the brain. Also, generally reduced levels of activity during SWS may result in flatter signals slowly changing and low-amplitude, further confounded by mixing which may be easier to extrapolate from.
Based on this observation, we initially analyzed single neuron spike train data obtained during sleep and awake states by Steriade et al. Our results were consistent with what we reported here, however, the data set was very small on the order of spikes per condition, compared to thousands of peaks in the EEG data so we could not draw meaningful conclusions. However, since we found that using discrete events spikes instead of the continuous wave form gave promising results, we tried to recover such events in the EEG data which led us to the inter-peak interval IPI measure.
However, ERPs are by definition event-related, thus they are anchored to specific tasks or stimuli. Furthermore, ERPs are averages of over large number of trials. Due to these reasons, ERPs may not be suitable for studying ongoing baseline states such as awake, dreaming, or sleep, although they may be effective in detecting transition events between these on-going states Ogilvie et al.
AWAKE for subject 4 ]. Does this mean that subjects are more conscious during REM sleep than when they are awake? The reason for this may again be due to the mixed nature of EEG signals, plus the natural sources of randomness in the stimulus environment during the awake state. Because the awake EEG signals are driven both by the internal brain dynamics and the external stimuli, a mixture of the two may be slightly less predictable.
A possible way to isolate the internal vs. This way, we can rule out the externally driven signal variability during awake state. Our prediction is that the predictability of these internal components would be as high as that of the REM data. Another interesting property of the IPI prediction error distribution is its positive skewness under all conditions Figure 4.
One possible explanation for this is that the prediction mechanism may be tuned more to shorter IPIs as the EEG signals generally tend to show high-frequency bursts followed by occasional pause of low-frequency intervals. The IPI distribution itself Figure 6 shows that, for all cases, the distributions are positively skewed, and so the number of IPI values smaller than the mean is more frequent than those with values larger than the mean.
This trend can explain the positive skewness of the IPI prediction error. Finally, we would like to discuss briefly some implications of our results on time perception and neurorobotics. First, the very existence of such regular and predictable internal dynamics could be a foundation for time perception mechanisms, for example, as a pace maker or a internal metric against which order and duration Wittmann and Paulus, ; Maniadakis et al.
A deeper understanding of this connection can lead to robust time perception and control mechanisms for neurorobotics. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. We would like to thank the reviewers for their detailed suggestions. Artola, A. Long-term potentiation and NMDA receptors in rat visual cortex. Nature , — Berger, H.
Psychiatry Clin. Bi, G. Activity-induced synaptic modifications in hippocampal culture: dependence on spike timing, synaptic strength and cell type. Pubmed Abstract Pubmed Full Text. Blinowska, K. Non-linear and linear forecasting of the eeg time series. Bongard, J. Resilient machines through continuous self-modeling. Science , — Cavallero, C. Slow wave sleep dreaming. Sleep 15, — Choe, Y. Time, consciousness, and mind uploading.
CrossRef Full Text. Chung, J. Springer series in cognitive and neural systems, Vol. Rao and G. Cohen, J. Statistical Power Analysis for the Behavioral Sciences. Coyle, D. A time-series prediction approach for feature extraction in a brain-computer interface. IEEE Trans. Neural Syst. Crick, F. Dainton, B. Daprati, E.
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Goldberger, A. Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals. Circulation , e—e Graziano, M. Complex movements evolved by microstimulation of precentral cortex. Neuron 34, — Gross, H. Generative character of perception: a neural architecture for sensorimotor anticipation.
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