Analysing the Thought Process of EEG Pattern using Compressed Sensing Architecture
Arun Kumar S
Department of Computer Science and Engineering, School of Computing, SRM Institute of Science and Technology Potheri, Kattankulathur, Tamil Nadu, India.
Sleep pattern recognition plays a crucial role in detecting pathological and psychological diseases. Various disorders can be identified through analysis of EEG patterns recorded during sleep. Sleep EEG consists of four primary waveforms: alpha, beta, theta, and delta waves, each associated with different sleep stages. The cyclic alternating pattern (CAP) is characterized by cerebral activity and autonomic motor functions, providing insights into motor events and neurovegetative functions that aid in understanding the pathophysiology of sleep disorders. This research focuses on identifying sleep patterns using a Compressed Sensing Architecture (CSA). The aim is to assist pathologists in accurately and efficiently diagnosing sleep disorders through automated analysis. Existing methodologies for extracting sleep patterns from EEG rely on various algorithms. In this study, error signals are extracted using CSA, and metrics such as the Percentage Root-mean-square Difference (PRD) and Signal-to-Noise Ratio (SNR) are computed after reconstructing the original signal. The proposed approach demonstrates enhanced accuracy, making it a promising solution for automated, error-free diagnosis of sleep disorders. The research findings have significant potential for practical implementation, improving diagnostic precision and clinical outcomes.
G. Cattan, “The Use of Brain–Computer Interfaces inGames IsNot Ready for the General Public,” Frontiers in Computer Science, vol. 3,Mar. 2021, doi: 10.3389/fcomp.2021.628773.
C. Gezgez and E. Kacar, “Virtual Character Control by Brain-Computer Interface and Comparison of Performance Metrics,” 2021International Conference on INnovations in Intelligent SysTems and Applications (INISTA), pp. 1–7, Aug.2021, doi:10.1109/inista52262.2021.9548604.
T. Roopa Rechal, P. Rajesh Kumar,and Sk. Ebraheem Khaleelulla, “A Feasibility Approach in DiagnosingASD with PIE via Machine Learning Classification Approach using BCI,” 2021 International Conference on Computing, Communication, and Intelligent Systems(ICCCIS), pp. 557–562, Feb. 2021, doi: 10.1109/icccis51004.2021.9397220.
M. Ranjani and P. Supraja, “Classifying the Autism and Epilepsy Disorder Based on EEG Signal Using Deep Convolutional Neural Network (DCNN),” 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 880–886, Mar. 2021, doi: 10.1109/icacite51222.2021.9404634.
S. D. T. Olesen, R. Das, M. D. Olsson, M. A. Khan, and S. Puthusserypady, “Hybrid EEG-EOG-based BCI system for Vehicle Control,” 2021 9th International Winter Conference on Brain-Computer Interface (BCI), pp. 1–6, Feb. 2021, doi: 10.1109/bci51272.2021.9385300.
J. Pradeepkumar, M. Anandakumar, V. Kugathasan, T. D. Lalitharatne, A. C. De Silva, and S. L. Kappel, “Decoding of Hand Gestures from Electrocorticography with LSTM Based Deep Neural Network,” 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 420–423, Nov. 2021, doi: 10.1109/embc46164.2021.9630958.
D. Dash, A. Wisler, P. Ferrari, E. M. Davenport, J. Maldjian, and J. Wang, “MEG Sensor Selection for Neural Speech Decoding,” IEEE Access, vol. 8, pp. 182320–182337, 2020, doi: 10.1109/access.2020.3028831.
X. Zhang, L. Yao, Q. Z. Sheng, S. S. Kanhere, T. Gu, and D. Zhang, “Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals,” 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 1–10, Mar. 2018, doi: 10.1109/percom.2018.8444575.
E. Petoku and G. Capi, “Object Movement Motor Imagery for EEG based BCI System using Convolutional Neural Networks,” 2021 9th International Winter Conference on Brain-Computer Interface (BCI), pp. 1–5, Feb. 2021, doi: 10.1109/bci51272.2021.9385319.
G. Huve, K. Takahashi, and M. Hashimoto, “Brain-computer interface using deep neural network and its application to mobile robot control,” 2018 IEEE 15th International Workshop on Advanced Motion Control (AMC), pp. 169–174, Mar. 2018, doi: 10.1109/amc.2019.8371082.
M. F. Ansari, D. R. Edla, S. Dodia, and V. Kuppili, “Brain-Computer Interface for wheelchair control operations: An approach based on Fast Fourier Transform and On-Line Sequential Extreme Learning Machine,” Clinical Epidemiology and Global Health, vol. 7, no. 3, pp. 274–278, Sep. 2019, doi: 10.1016/j.cegh.2018.10.007.
N. Thillaiarasu, S. Lata Tripathi, and V. Dhinakaran, Artificial Intelligence for Internet of Things. CRC Press, 2022. doi: 10.1201/9781003335801.
A. Bablani, D. R. Edla, D. Tripathi, and R. Cheruku, “Survey on Brain-Computer Interface,” ACM Computing Surveys, vol. 52, no. 1, pp. 1–32, Feb. 2019, doi: 10.1145/3297713.
M. Bueno-Lopez, P. A. Munoz-Gutierrez, E. Giraldo and M. Molinas, “Electroencephalographic Source Localization based on Enhanced Empirical Mode Decomposition,” IAENG International Journal of Computer Science, Vol. 46, No. 2, pp. 228-236, (2019).
N. F. Ozkan and E. Kahya, “Classification of BCI Users Based on Cognition,” Computational Intelligence and Neuroscience, vol. 2018, pp. 1–10, 2018, doi: 10.1155/2018/6315187.
A. Chowdhury, H. Raza, Y. K. Meena, A. Dutta, and G. Prasad, “An EEG-EMG correlation-based brain-computer interface for hand orthosis supported neuro-rehabilitation,” Journal of Neuroscience Methods, vol. 312, pp. 1–11, Jan. 2019, doi: 10.1016/j.jneumeth.2018.11.010.
M. C. Massi and F. Ieva, “Learning Signal Representations for EEG Cross-Subject Channel Selection and Trial Classification,” 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–6, Oct. 2021, doi: 10.1109/mlsp52302.2021.9596522.
J. Onners, M. Alam, B. Cichy, N. Wymbs, and J. Lukos, “U-EEG: A Deep Learning Autoencoder for the Detection of Ocular Artifact in EEG Signal,” 2021 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), pp. 1–5, Dec. 2021, doi: 10.1109/spmb52430.2021.9672271.
CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Arun Kumar S and Anand L;
Methodology: Arun Kumar S;
Software: Arun Kumar S and Anand L;
Data Curation: Anand L;
Writing- Original Draft Preparation: Arun Kumar S and Anand L;
Visualization: Arun Kumar S;
Investigation: Anand L;
Supervision: Arun Kumar S and Anand L;
Validation: Arun Kumar S;
Writing- Reviewing and Editing: Arun Kumar S and Anand L;
All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
Author(s) thanks to Dr. Anand L for this research completion and support.
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Corresponding author
Anand L
Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology Potheri, Kattankulathur, Tamil Nadu, India.
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Cite this article
Arun Kumar S and Anand L, “Analysing the Thought Process of EEG Pattern using Compressed Sensing Architecture”, Journal of Machine and Computing, pp. 671-681, April 2025, doi: 10.53759/7669/jmc202505053.
