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Predictable internal brain dynamics in EEG and its relation to conscious states.

Yoo J, Kwon J, Choe Y - Front Neurorobot (2014)

Bottom Line: Our results show that EEG signals from awake or rapid eye movement (REM) sleep states have more predictable dynamics compared to those from slow-wave sleep (SWS).Since awakeness and REM sleep are associated with conscious states and SWS with unconscious or less consciousness states, these results support our hypothesis.The results suggest an intricate relationship among prediction, consciousness, and time, with potential applications to time perception and neurorobotics.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, Texas A&M University College Station, TX, USA.

ABSTRACT
Consciousness is a complex and multi-faceted phenomenon defying scientific explanation. Part of the reason why this is the case is due to its subjective nature. In our previous computational experiments, to avoid such a subjective trap, we took a strategy to investigate objective necessary conditions of consciousness. Our basic hypothesis was that predictive internal dynamics serves as such a condition. This is in line with theories of consciousness that treat retention (memory), protention (anticipation), and primary impression as the tripartite temporal structure of consciousness. To test our hypothesis, we analyzed publicly available sleep and awake electroencephalogram (EEG) data. Our results show that EEG signals from awake or rapid eye movement (REM) sleep states have more predictable dynamics compared to those from slow-wave sleep (SWS). Since awakeness and REM sleep are associated with conscious states and SWS with unconscious or less consciousness states, these results support our hypothesis. The results suggest an intricate relationship among prediction, consciousness, and time, with potential applications to time perception and neurorobotics.

No MeSH data available.


Related in: MedlinePlus

EEG IPI prediction error distribution. The IPI prediction error distribution is shown for all four subjects, each for all three conditions (awake [red], REM [blue], and SWS [green]). 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. (A) Subject 1, (B) subject 2, (C) subject 3, (D) subject 4.
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Figure 4: EEG IPI prediction error distribution. The IPI prediction error distribution is shown for all four subjects, each for all three conditions (awake [red], REM [blue], and SWS [green]). 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. (A) Subject 1, (B) subject 2, (C) subject 3, (D) subject 4.

Mentions: The IPI prediction error on novel data (not used during training or validation) are summarized in Figure 3 and the error distributions shown in Figure 4.


Predictable internal brain dynamics in EEG and its relation to conscious states.

Yoo J, Kwon J, Choe Y - Front Neurorobot (2014)

EEG IPI prediction error distribution. The IPI prediction error distribution is shown for all four subjects, each for all three conditions (awake [red], REM [blue], and SWS [green]). 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. (A) Subject 1, (B) subject 2, (C) subject 3, (D) subject 4.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4043151&req=5

Figure 4: EEG IPI prediction error distribution. The IPI prediction error distribution is shown for all four subjects, each for all three conditions (awake [red], REM [blue], and SWS [green]). 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. (A) Subject 1, (B) subject 2, (C) subject 3, (D) subject 4.
Mentions: The IPI prediction error on novel data (not used during training or validation) are summarized in Figure 3 and the error distributions shown in Figure 4.

Bottom Line: Our results show that EEG signals from awake or rapid eye movement (REM) sleep states have more predictable dynamics compared to those from slow-wave sleep (SWS).Since awakeness and REM sleep are associated with conscious states and SWS with unconscious or less consciousness states, these results support our hypothesis.The results suggest an intricate relationship among prediction, consciousness, and time, with potential applications to time perception and neurorobotics.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Engineering, Texas A&M University College Station, TX, USA.

ABSTRACT
Consciousness is a complex and multi-faceted phenomenon defying scientific explanation. Part of the reason why this is the case is due to its subjective nature. In our previous computational experiments, to avoid such a subjective trap, we took a strategy to investigate objective necessary conditions of consciousness. Our basic hypothesis was that predictive internal dynamics serves as such a condition. This is in line with theories of consciousness that treat retention (memory), protention (anticipation), and primary impression as the tripartite temporal structure of consciousness. To test our hypothesis, we analyzed publicly available sleep and awake electroencephalogram (EEG) data. Our results show that EEG signals from awake or rapid eye movement (REM) sleep states have more predictable dynamics compared to those from slow-wave sleep (SWS). Since awakeness and REM sleep are associated with conscious states and SWS with unconscious or less consciousness states, these results support our hypothesis. The results suggest an intricate relationship among prediction, consciousness, and time, with potential applications to time perception and neurorobotics.

No MeSH data available.


Related in: MedlinePlus