Unveiling Neurophysiological Markers of Consciousness Levels through EEG Exploration


  • Jingming Gong Graduate School of Engineering, Saitama Institute of Technology, Fukaya City, Saitama, Japan
  • Linfeng Sui Graduate School of Engineering, Saitama Institute of Technology, Fukaya City, Saitama, Japan
  • Ran Zhang Graduate School of Engineering, Saitama Institute of Technology, Fukaya City, Saitama, Japan
  • Boning Li Graduate School of Engineering, Saitama Institute of Technology, Fukaya City, Saitama, Japan
  • Chengyuan Shen Graduate School of Engineering, Saitama Institute of Technology, Fukaya City, Saitama, Japan
  • ,Taiyo Maeda Graduate School of Engineering, Saitama Institute of Technology, Fukaya City, Saitama, Japan
  • Jianting Cao RIKEN Center for Advanced Intelligence Project (AIP), Chuo-ku, Tokyo, Japan




EEG, Approximate Entropy, Consciousness Levels


The concept of consciousness levels typically refers to various aspects and tiers related to an individual’s cognition, perception, thinking, and awareness. Although neurophysiological markers have not yet been definitively identified to distinguish between these nuanced levels, this paper introduces a robust marker, the Approximate Entropy (ApEn), which quantifies the complexity of EEG signals to differentiate states of altered consciousness. Utilizing ApEn, we analyze EEG data from the frontal lobe—a region closely associated with consciousness—in states indicative of severely altered conditions, specifically anesthesia, coma, and brain death. To enhance the precision of consciousness level
assessment, we employ a Support Vector Machine (SVM) model, which classifies the states based on EEG complexity measures. This approach not only provides valuable insights into the neural correlations associated with changes in these critical states but also underscores the potential of combining quantitative EEG analysis with machine learning techniques to advance our understanding of consciousness. The findings demonstrate that EEG complexity, when analyzed using ApEn coupled with SVM classification, offers a novel and effective method for assessing and distinguishing between degrees of consciousness. This approach promises significant implications for clinical diagnostics and patient monitoring.


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A Definition of Irreversible Coma: Report of the Ad Hoc Committee of the Harvard Medical School to Examine the

Definition of Brain Death. (1968). JAMA, 205 (6), 337–340. https://doi.org/10.1001/jama.1968.03140320031009

Bigdely-Shamlo, N., Mullen, T., Kothe, C., Su, K.-M., & Robbins, K. A. (2015). The prep pipeline: Standardized

preprocessing for large-scale eeg analysis. Frontiers in neuroinformatics, 9, 16. https://doi.org/10.3389/fninf.


Cao, J. (2006). Analysis of the quasi-brain-death eeg data based on a robust ica approach. International conference on

knowledge-based and intelligent information and engineering systems, 1240–1247. https://doi.org/10.1007/


Cao, J., & Chen, Z. (2008). Advanced eeg signal processing in brain death diagnosis. In Signal processing techniques

for knowledge extraction and information fusion (pp. 275–298). Springer. https://doi.org/10.1007/978-0-387-


Chang, C.-C., & Lin, C.-J. (2011). Libsvm: A library for support vector machines. ACM transactions on intelligent

systems and technology (TIST), 2 (3), 1–27. https://doi.org/10.1145/1961189.1961199

Cohen, M. X. (2014). Analyzing neural time series data: Theory and practice. MIT press.

Cui, G., Zhu, L., Zhao, Q., Cao, J., & Cichocki, A. (2017). A graph theory analysis on distinguishing eeg-based brain death

and coma. Neural Information Processing: 24th International Conference, ICONIP 2017, Guangzhou, China,

November 14–18, 2017, Proceedings, Part IV 24, 589–595. https://doi.org/10.1007/978-3-319-70093-9_62

Darracq, M., Funk, C. M., Polyakov, D., Riedner, B., Gosseries, O., Nieminen, J. O., Bonhomme, V., Brichant, J.-F.,

Boly, M., Laureys, S., et al. (2018). Evoked alpha power is reduced in disconnected consciousness during sleep

and anesthesia. Scientific reports, 8 (1), 16664. https://doi.org/10.1038/s41598-018-34957-9

Drucker, H., Burges, C. J., Kaufman, L., Smola, A., & Vapnik, V. (1996). Support vector regression machines. Advances

in neural information processing systems, 9.

Jennett, B., & Plum, F. (1972). Persistent vegetative state after brain damage: A syndrome in search of a name

[Originally published as Volume 1, Issue 7753]. The Lancet, 299 (7753), 734–737. https://doi.org/10.1016/S0140-


Laureys, S., Celesia, G. G., Cohadon, F., Lavrijsen, J., León-Carrión, J., Sannita, W. G., Sazbon, L., Schmutzhard, E.,

von Wild, K. R., Zeman, A., et al. (2010). Unresponsive wakefulness syndrome: A new name for the vegetative

state or apallic syndrome. BMC medicine, 8 (1), 1–4. https://doi.org/10.1186/1741-7015-8-68

Liu, Q., Ma, L., Fan, S.-Z., Abbod, M. F., & Shieh, J.-S. (2018). Sample entropy analysis for the estimating depth of

anaesthesia through human eeg signal at different levels of unconsciousness during surgeries. PeerJ, 6, e4817.


Liu, W., Thorp, T., Graham, S., & Aitkenhead, A. (1991). Incidence of awareness with recall during general anaesthesia.

Anaesthesia, 46 (6), 435–437. https://doi.org/doi.org/10.1111/j.1365-2044.1991.tb11677.x

Marks, S. J., & Zisfein, J. (1990). Apneic oxygenation in apnea tests for brain death a controlled trial. Archives of

neurology, 47 (10), 1066–1068. https://doi.org/10.1001/archneur.1990.00530100028009

Mashour, G. A., & Hudetz, A. G. (2018). Neural correlates of unconsciousness in large-scale brain networks. Trends in

neurosciences, 41 (3), 150–160. https://doi.org/10.1016/j.tins.2018.01.003

Pincus, S. M. (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of

Sciences, 88 (6), 2297–2301. https://doi.org/10.1073/pnas.88.6.2297

Sanders, R. D., Tononi, G., Laureys, S., Sleigh, J. W., & Warner, D. S. (2012). Unresponsiveness̸= unconsciousness.

The Journal of the American Society of Anesthesiologists, 116 (4), 946–959. https://doi.org/10.1097/ALN.


Schiff, N. D. (2010). Recovery of consciousness after brain injury: A mesocircuit hypothesis. Trends in neurosciences,

(1), 1–9. https://doi.org/10.1016/j.tins.2009.11.002

Scott, J. B., Gentile, M. A., Bennett, S. N., Couture, M., & MacIntyre, N. R. (2013). Apnea testing during brain

death assessment: A review of clinical practice and published literature. Respiratory care, 58 (3), 532–538.


Sebel, P. S., Bowdle, T. A., Ghoneim, M. M., Rampil, I. J., Padilla, R. E., Gan, T. J., & Domino, K. B. (2004). The

incidence of awareness during anesthesia: A multicenter united states study. Anesthesia & Analgesia, 99 (3),

–839. https://doi.org/110.1213/01.ANE.0000130261.90896.6C

Shi, Q., Cao, J., Zhou, W., Tanaka, T., & Wang, R. (2010). Dynamic extension of approximate entropy measure for

brain-death eeg. Advances in Neural Networks-ISNN 2010: 7th International Symposium on Neural Networks,

ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part II 7, 353–359. https://doi.org/10.1007/978-3-


Singer, W. (2007). Binding by synchrony. Scholarpedia, 2 (12), 1657. https://doi.org/10.4249/scholarpedia.1657

Szurhaj, W., Lamblin, M.-D., Kaminska, A., & Sediri, H. (2015). Eeg guidelines in the diagnosis of brain death.

Neurophysiologie Clinique/Clinical Neurophysiology, 45 (1), 97–104. https://doi.org/10.1016/j.neucli.2014.11.005

Teasdale, G., & Jennett, B. (1974). Assessment of coma and impaired consciousness: A practical scale. The Lancet,

(7872), 81–84. https://doi.org/10.1016/S0140-6736(74)91639-0

van Oud-Alblas, H. B., van Dijk, M., Liu, C., Tibboel, D., Klein, J., & Weber, F. (2008). Intraoperative awareness

during pediatric anesthesia. Anesth Analg, 107, 1536Y1541. https://doi.org/10.1097/SA.0b013e3181b7d6c2




How to Cite

Gong, J. ., Sui, L., Zhang, R., Li, B., Shen, C. . . ., Maeda, ,Taiyo ., & Cao, J. . (2024). Unveiling Neurophysiological Markers of Consciousness Levels through EEG Exploration. INTERNATIONAL JOURNAL OF COMPUTERS &Amp; TECHNOLOGY, 24, 42–52. https://doi.org/10.24297/ijct.v24i.9627



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