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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 Data. EEG data (Kemp et al., 2000) from the PhysioBank (Goldberger et al., 2000) are shown. Each row represents data from each subject (four total) and each column represents different states. (A) Awake, raw data. (B) Awake, smoothed (Gaussian filter, σ = 1), and peaks identified (circles). (C) REM, raw data. (D) REM, smoothed and peaks identified. (E) SWS, raw data. (F) SWS, smoothed and peaks identified. Each data set had 30,000 data points but here we are showing only the first 1000 for a better view of the details.
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Figure 1: EEG Data. EEG data (Kemp et al., 2000) from the PhysioBank (Goldberger et al., 2000) are shown. Each row represents data from each subject (four total) and each column represents different states. (A) Awake, raw data. (B) Awake, smoothed (Gaussian filter, σ = 1), and peaks identified (circles). (C) REM, raw data. (D) REM, smoothed and peaks identified. (E) SWS, raw data. (F) SWS, smoothed and peaks identified. Each data set had 30,000 data points but here we are showing only the first 1000 for a better view of the details.

Mentions: Figure 1 shows the EEG data sets we used for our analysis, from Kemp et al. (2000). We used EEG signals from four subjects with recordings during awake state (A,B), REM sleep (C,D), and SWS (E,F). We convolved the EEG signal with a Gaussian filter with σ = 1 to smooth the signals. This was done to avoid sharp, high frequency peaks that made prediction difficult in all conditions (awake, REM, and SWS).


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

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

EEG Data. EEG data (Kemp et al., 2000) from the PhysioBank (Goldberger et al., 2000) are shown. Each row represents data from each subject (four total) and each column represents different states. (A) Awake, raw data. (B) Awake, smoothed (Gaussian filter, σ = 1), and peaks identified (circles). (C) REM, raw data. (D) REM, smoothed and peaks identified. (E) SWS, raw data. (F) SWS, smoothed and peaks identified. Each data set had 30,000 data points but here we are showing only the first 1000 for a better view of the details.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: EEG Data. EEG data (Kemp et al., 2000) from the PhysioBank (Goldberger et al., 2000) are shown. Each row represents data from each subject (four total) and each column represents different states. (A) Awake, raw data. (B) Awake, smoothed (Gaussian filter, σ = 1), and peaks identified (circles). (C) REM, raw data. (D) REM, smoothed and peaks identified. (E) SWS, raw data. (F) SWS, smoothed and peaks identified. Each data set had 30,000 data points but here we are showing only the first 1000 for a better view of the details.
Mentions: Figure 1 shows the EEG data sets we used for our analysis, from Kemp et al. (2000). We used EEG signals from four subjects with recordings during awake state (A,B), REM sleep (C,D), and SWS (E,F). We convolved the EEG signal with a Gaussian filter with σ = 1 to smooth the signals. This was done to avoid sharp, high frequency peaks that made prediction difficult in all conditions (awake, REM, and SWS).

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