Citation :

Saranjuu Chulakit, Amirul Syafiq Sadun, Nor Anija Jalaludin, Thiva Surena Krishnah Rao, "Development of a Cost-Effective EEG System for Real-Time Monitoring of Cognitive States in Noisy Environments," International Journal of Engineering Trends and Technology (IJETT), vol. 73, no. 12, pp. 52-60, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I12P105

Abstract

This research proposes a new design and testing of a low-cost EEG-based system to monitor a person's mental condition in a real-time environment under different noise conditions. The novelty designed uses a NeuroSky Mindwave headset and uses LabVIEW software as an interface to assemble the data, process it using Fast Fourier Transform (FFT), and display the EEG in real time. The research objectives are to assess attention and meditation states during task execution, and then the system has been tested on five participants performing a structured Lego assembly task in both controlled and loud noise environments. The result shows that under loud noise, mean alpha and beta activity nearly doubled (α: 11.3% vs. 5.4%, β: 6.4% vs. 3.4%), delta activity decreased significantly (59.5% vs. 72.6%, p = 0.019), and attention scores rose markedly (60 ± 15 vs. 26 ± 18, p = 0.05). It can be shown that the system’s ability to depict variations in brainwave dynamics and cognitive states. The study validates the feasibility of consumer-grade EEG systems for real-time cognitive monitoring in noisy environments and highlights their potential applications in education, training, and ambient interface research.

Keywords

Brain Computer Interface (BCI), Low-cost EEG system, Attention, Meditation.

References

[1] Mona Algarni, and Faisal Saeed, “Review on Emotion Recognition Using EEG Signals based on Brain-Computer Interface System,” International Conference of Reliable Information and Communication Technology, Springer, Cham, Langkawi, Malaysia, pp. 449-461, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[2] Marwan Nafea et al., “Brainwave-Controlled System for Smart Home Applications,” 2018 2^nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS), Kuching, Malaysia, pp. 75-80, 2018.
[CrossRef] [Google Scholar] [Publisher Link]

[3] Li Yi Qin et al., “Smart Home Control for Disabled using Brain Computer Interface,” International Journal of Integrated Engineering, vol. 12, no. 4, pp. 74-82, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[4] Syed M. Saddique, and Laraib Hassan Siddiqui, “EEG Based Brain Computer Interface,” Journal of Software, vol. 4, no. 6, pp. 550-554, 2009.
[CrossRef] [Google Scholar] [Publisher Link]

[5] Reza Fazel-Rezai et al., “P300 Brain Computer Interface: Current Challenges and Emerging Trends,” Frontiers in Neuroengineering, vol. 5, pp. 1-14, 2012.
[CrossRef] [Google Scholar] [Publisher Link]

[6] Ashima Khosla, Padmavati Khandnor, and Trilok Chand, “A Comparative Analysis of Signal Processing and Classification Methods for Different Applications based on EEG Signals,” Biocybernetics and Biomedical Engineering, vol. 40, no. 2, pp. 649-690, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[7] N. Veena, and N. Anitha, “A Review of Non-Invasive BCI Devices,” International Journal Biomedical Engineering and Technology, vol. 34, no. 3, pp. 205-233, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[8] Sarah N. Abdulkader, Ayman Atia, and Mostafa-Sami M. Mostafa, “Brain Computer Interfacing: Applications and Challenges,” Egyptian Informatics Journal, vol. 16, no. 2, pp. 213-230, 2015.
[CrossRef] [Google Scholar] [Publisher Link]

[9] Maggie S.M. Chow et al., “Functional Magnetic Resonance Imaging and the Brain: A Brief Review,” World Journal of Radiology, vol. 9, no. 1, pp. 5-9, 2017.
[CrossRef] [Google Scholar] [Publisher Link]

[10] James E. Niemeyer, “Brain-Machine Interfaces: Assistive, Thought-Controlled Devices,” Lab Anim, vol. 45, no. 10, pp. 359-361, 2016.
[CrossRef] [Google Scholar] [Publisher Link]

[11] Mahsa Soufineyestani, Dale Dowling, and Arshia Khan, “Electroencephalography (EEG) Technology Applications and Available Devices,” Applied Science, vol. 10, no. 21, pp. 1-23, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[12] J. Katona et al., “Evaluation of the Neurosky Mindflex EEG Headset Brain Waves Data,” 2014 IEEE 12^th International Symposium on Applied Machine Intelligence and Informatics (SAMI), Herl'any, Slovakia, pp. 91-94, 2014.
[CrossRef] [Google Scholar] [Publisher Link]

[13] Shakshi, and Ramavtar Jaswal, “Brain Wave Classification and Feature Extraction of EEG Signal by using FFT on Lab View,” International Research Journal of Engineering and Technology, vol. 3, no. 7, pp. 1208-1212, 2016.
[Google Scholar] [Publisher Link]

[14] Michal Teplan, “Fundamentals of EEG Measurement,” Measurement Science Review, vol. 2, no. 2, pp. 1-11, 2002.
[Google Scholar] [Publisher Link]

[15] Rahmah Mokhtar et al., “Assessing Attention and Meditation Levels in Learning Process using Brain Computer Interface,” Advance Science Letter, vol. 23, no. 6, pp. 1-5, 2017.
[CrossRef] [Google Scholar] [Publisher Link]

[16] S. Pawar, S.R. Chougule, and A.H. Tirmare, “Diagnosis of Epilepsy a Neurological Disorder using Electroencephalogram (EEG),” International Journal of Modern Trends in Engineering and Research, vol. 4, no. 6, pp. 144-149, 2017.
[Google Scholar]