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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 distribution. (A–D) The IPI distributions are shown for all four subjects, for all three conditions (awake [red], REM [blue], and SWS [green]). For all cases, the IPI distributions are positively skewed. The skewness varied from 0.83 to 2.71. The x-axis represents time (unit = 10 ms) and the y-axis frequency. (A) Subject 1, (B) subject 2, (C) subject 3, (D) subject 4.
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Figure 6: EEG IPI distribution. (A–D) The IPI distributions are shown for all four subjects, for all three conditions (awake [red], REM [blue], and SWS [green]). For all cases, the IPI distributions are positively skewed. The skewness varied from 0.83 to 2.71. The x-axis represents time (unit = 10 ms) and the y-axis frequency. (A) Subject 1, (B) subject 2, (C) subject 3, (D) subject 4.

Mentions: There were a couple of interesting properties we observed in the results. First, REM data had the lowest IPI prediction error, even compared to the awake state. 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.e., the distribution is positively skewed, with skewness ranging from 0.86 to 1.69, Figure 4). Since the error is calculated as error = true − predicted, underestimation of the IPI seems more error-prone than overestimation. 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.


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

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

EEG IPI distribution. (A–D) The IPI distributions are shown for all four subjects, for all three conditions (awake [red], REM [blue], and SWS [green]). For all cases, the IPI distributions are positively skewed. The skewness varied from 0.83 to 2.71. The x-axis represents time (unit = 10 ms) and the y-axis frequency. (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 6: EEG IPI distribution. (A–D) The IPI distributions are shown for all four subjects, for all three conditions (awake [red], REM [blue], and SWS [green]). For all cases, the IPI distributions are positively skewed. The skewness varied from 0.83 to 2.71. The x-axis represents time (unit = 10 ms) and the y-axis frequency. (A) Subject 1, (B) subject 2, (C) subject 3, (D) subject 4.
Mentions: There were a couple of interesting properties we observed in the results. First, REM data had the lowest IPI prediction error, even compared to the awake state. 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.e., the distribution is positively skewed, with skewness ranging from 0.86 to 1.69, Figure 4). Since the error is calculated as error = true − predicted, underestimation of the IPI seems more error-prone than overestimation. 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.

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