Conference articles · 2023
A Chaos-based Non-linear Analysis Method for Detecting Human Attention Levels in EEG Signals
2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE) · Pages 201–204
Abstract
The paper presents chaos-theory-based human attention level detection from the electroencephalogram (EEG) signals. In the medical field, “human attention level” can be referred to as the “attentional state,”, which helps to understand an individual’s attention capacity in various crucial moments. In this study, we have deployed secondary analysis on existing methods by implementing chaos theory on the PhysioNet dataset. We investigate different vital parameters and values to predict human attention level from the EEG dataset. We calculated time delay, embedding dimension, and correlation dimension from the participants’ EEG data to determine the parameters’ values: specifically, for detecting of human attention level from EEG signals. By calculating the 95% confidence interval (CI), the time delay has an average of 2.50 seconds, and the embedding dimension and correlation dimension have an average value of 4.41 and 2.23, respectively. We also observed a similar embedded signal in the reconstructed phase space (RPS) of participant’s EEG signals. The statistical and chaos-based plot can potentially investigate human attention parameters and develop a robust EEG signal prediction system. Overall, this proposed framework serves as a resource on the latest nonlinearity detection techniques to detect human attention levels utilizing EEG signal analysis.
Publication details
- Category
- Conference articles
- Authors & affiliations
- Venue
- 2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE)
- Publisher
- IEEE
- Record
- Pages 201–204
- Published
- 2023
- Cite this work
Masud Rabbani, Sayed Mashroor Mamun, Parama Sridevi, Iysa Iqbal, Anubhav Parida, Anushka Kolli, Hansika Kolli, M. Rubaiyat Hossain Mondal, Mohammad Aftab Rassel, Enayet Hossain, Farhad Ahmed, Sheikh Iqbal Ahamed. “A Chaos-based Non-linear Analysis Method for Detecting Human Attention Levels in EEG Signals”. 2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE), 201–204. (2023). https://doi.org/10.1109/BIBE60311.2023.00039.
Plain-language summary
Research takeaway
This study applies chaos-theory parameters to a PhysioNet EEG dataset to investigate human attention levels. It estimates time delay, embedding dimension, and correlation dimension and examines reconstructed phase-space patterns.
Research topics
- Chaos Theory
- Nonlinear Analysis
- Electroencephalography (EEG)
- Correlation Dimension
- Attention Level
- EEG
- attention detection
- chaos theory
- nonlinear analysis
- cognitive state
- brain-signal processing
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