Unveiling Neurophysiological Markers of Consciousness Levels through EEG Exploration

Authors

  • 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

DOI:

https://doi.org/10.24297/ijct.v24i.9627

Keywords:

EEG, Approximate Entropy, Consciousness Levels

Abstract

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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Published

2024-06-05

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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