Evaluating the role channel selection in EEG anxiety recognition rates utilizing a chaotic map
Abstract
Purpose: Today, the human lifestyle has led to an increase in anxiety. Its diagnosis is usually made with questionnaires and by specialist physicians. Recently, objective techniques such as brain-behavior analysis have captivated the attention of scientists for the early detection of this disorder. This study aimed to provide a method for diagnosing anxiety based on electroencephalogram (EEG) signals. Also, presents a new methodology by examining different approaches to brain channel selection and feature extraction based on chaotic maps.
Materials and Methods: The DASPS database was used, containing a 14-channel EEG of 23 people (10 men and 13 women, average age: 30 years). The self-assessment manikin was applied to divide anxiety into 2 and 4 levels. Firstly, four methods were assessed to select the optimal channel; two methods were based on the minimum coefficient of variation, and two methods were based on the maximum relative power. Then, Chebyshev’s chaotic map was reconstructed, and two features, including 1) the maximum density and 2) its corresponding sample, were extracted. Finally, the k-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) classifiers were applied.
Results: The results indicated a maximum accuracy of 100% for both two/four-level anxiety detection. In addition, the K-NN outperformed the SVM classifier.
Conclusion: It highlighted the role of some brain channels, as well as the classifier structure, in distinguishing anxiety levels. The outstanding result of the proposed algorithm nominated it as a suitable approach for anxiety detection.
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29- Athar A Ein Shoka, Monagi H Alkinani, AS El-Sherbeny, Ayman El-Sayed, and Mohamed M Dessouky, "Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals." Brain Informatics, Vol. 8pp. 1-16, (2021).
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31- Carla Santos and Cristina Dias, "Note on the coefficient of variation properties." Brazilian Electronic Journal of Mathematics, Vol. 2 (No. 4), pp. 101-11, (2021).
32- Charles E Brown, Applied multivariate statistics in geohydrology and related sciences. Springer Science & Business Media, (2012).
33- Bikesh Kumar Singh, Kesari Verma, and AS Thoke, "Investigations on impact of feature normalization techniques on classifier's performance in breast tumor classification." International Journal of Computer Applications, Vol. 116 (No. 19), (2015).
34- Sang-Hong Lee, Joon S Lim, Jae-Kwon Kim, Junggi Yang, and Youngho Lee, "Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance." Computer methods and programs in biomedicine, Vol. 116 (No. 1), pp. 10-25, (2014).
35- RC Watt and SR Hameroff, "Phase space electroencephalography (EEG): a new mode of intraoperative EEG analysis." International journal of clinical monitoring and computing, Vol. 5pp. 3-13, (1988).
36- Zahra Barati Moghadam, Zahra Ghaemian, Najmeh Sahebalam, Ateke Goshvarpour, and Ghasem Sadeghi Bajestani, "Gender Differences in Cardiovascular Performance While Running at Maximum Heart Rate: Emphasis on Phase Space Dynamic." Journal of Applied Exercise Physiology, Vol. 16 (No. 31), pp. 71-87, (2020).
37- Morteza Zangeneh Soroush, Keivan Maghooli, Seyed Kamaledin Setarehdan, and Ali Motie Nasrabadi, "Emotion recognition through EEG phase space dynamics and Dempster-Shafer theory." Medical Hypotheses, Vol. 127pp. 34-45, (2019).
38- Yonghui Fang, Minyou Chen, and Xufei Zheng, "Extracting features from phase space of EEG signals in brain–computer interfaces." Neurocomputing, Vol. 151pp. 1477-85, (2015).
39- Minyou Chen, Yonghui Fang, and Xufei Zheng, "Phase space reconstruction for improving the classification of single trial EEG." Biomedical Signal Processing and Control, Vol. 11pp. 10-16, (2014).
40- Rajeev Sharma and Ram Bilas Pachori, "Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions." Expert Systems with Applications, Vol. 42 (No. 3), pp. 1106-17, (2015).
41- Dharminder Dharminder and Pratik Gupta, "Security analysis and application of Chebyshev Chaotic map in the authentication protocols." International Journal of Computers and Applications, Vol. 43 (No. 10), pp. 1095-103, (2021).
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43- Ali Kaveh, R Mahdipour Moghanni, and SM Javadi, "Chaotic optimization algorithm for performance-based optimization design of composite moment frames." Engineering with Computers, Vol. 38 (No. 3), pp. 2729-41, (2022).
44- Gaimei Gao, Xinguang Peng, Ye Tian, and Zefeng Qin, "A chaotic maps-based authentication scheme for wireless body area networks." International Journal of Distributed Sensor Networks, Vol. 12 (No. 7), p. 2174720, (2016).
45- Chandrashekhar Meshram, Mohammad S Obaidat, and Sarita G Meshram, "Chebyshev chaotic map‐based ID‐based cryptographic model using subtree and fuzzy‐entity data sharing for public key cryptography." Security and Privacy, Vol. 1 (No. 1), p. e12, (2018).
46- Santanu Chatterjee, Sandip Roy, Ashok Kumar Das, Samiran Chattopadhyay, Neeraj Kumar, and Athanasios V Vasilakos, "Secure biometric-based authentication scheme using Chebyshev chaotic map for multi-server environment." IEEE Transactions on Dependable and Secure Computing, Vol. 15 (No. 5), pp. 824-39, (2016).
47- Ali Shakiba, "Generating dynamical S-boxes using 1D Chebyshev chaotic maps." Journal of Computing and Security, Vol. 7 (No. 1), pp. 1-17, (2020).
48- Malcolm McLean. (2024). data density plot. [Online]. Available: http://www.mathworks.com/matlabcentral/fileexchange/31726-data-density-plot.
49- Yoshua Bengio and Yves Grandvalet, "No unbiased estimator of the variance of k-fold cross-validation." Advances in Neural Information Processing Systems, Vol. 16(2003).
50- Davide Anguita, Alessandro Ghio, Sandro Ridella, and Dario Sterpi, "K-Fold Cross Validation for Error Rate Estimate in Support Vector Machines." in DMIN, (2009), pp. 291-97.
51- Trent D Buskirk, Antje Kirchner, Adam Eck, and Curtis S Signorino, "An introduction to machine learning methods for survey researchers." Survey Practice, Vol. 11 (No. 1), (2018).
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| Files | ||
| Issue | Vol 13 No 2 (2026) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v13i2.21945 | |
| Keywords | ||
| Electroencephalography Anxiety Classification Chebyshev’s Chaotic Map Channel Selection | ||
| Rights and permissions | |
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Daneshmand-Bahman F, Goshvarpour A. Evaluating the role channel selection in EEG anxiety recognition rates utilizing a chaotic map. Frontiers Biomed Technol. 2026;13(2):472-491.
