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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM63399

ELECTROENCEPHALOGRAM-BASED MENTAL STRESS DETECTION

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63399

Volume:

10 |

5 |

May 2026

Pages:

1-5

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Soujanya N

Soujanya N

Master Of Computer Applications

Bapuji Institute Of Enginerring Technology

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Mentors

SKT

Siddesh K T

Master Of Computer Applications

Bapuji Institute Of Enginerring Technology

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Abstract

In the present fast-moving world, mental stress has become a rather widespread factor, leading to its widespread impacts on the physical and mental conditions of people. It can be clarified that avoiding physically and psychologically stressful situations is what defines mental stress. mentally. Thus, the development of the identification of mental stress would be the focus of the proposed project. the EEG technology where the calculation and analysis of waves in the brain are carried out in order to identify. the level of mental strain. Thus the technology would be employed in the process of acquiring the EEG headsets. EEG signal that will primarily aid in obtaining the brains electrical signals. Otherwise, it would be expected that the signals would produce Alpha Beta and Theta waves while ignoring noise. as well as Gamma Waves. Otherwise, the assumption would describe the waves as directly influencing the mental degree. There are three different levels of mental stress: low medium and high. This technology would otherwise be assumed to result in a relatively precise and efficient approach to the. obtaining and identifying mental stress with the aid of reasonably accurate and effective results. Otherwise, it would be assumed that the technology would use relatively to produce outputs and results automatically. simpler GUI principles. From now on it would be assumed that the technology would help with tracking the mental state. utilizing the real-time reports and outputs that were obtained. The technology would henceforth provide assistance in the development of the mental health status using the. assistance in making wise and reasonably effective decisions regarding mental stress management.

KEYWORDS

Keywords: EEG-based stress detection Artificial Intelligence Machine Learning Neural Networks Predictive Analytics

Cite This Article

Soujanya N, Siddesh K T, "ELECTROENCEPHALOGRAM-BASED MENTAL STRESS DETECTION," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-5, May 2026.

Full Article/Conclusion

Therefore, the EEG-based mental stress detection system has been successfully developed. First, the system. works using various functions that include classification of stress levels and the. presentation of results. This has been achieved by the fact that all these functions. have their importance in relation. to the main objective of the design which is accuracy in results. In this case, the stress level. of the individual will be categorized into. low medium and high. The results will be presented in a very. Moreover, the proposed. system will guide the individual in relaxation if the results indicate a certain. stress level. The system is more accurate, non-invasive and immediate than the previous approaches. used in mental stress detections due to the fact that the system. can measure the brain signal accurately. Finally, the EEG-based mental stress detection system. has been successful in mental stress detections. This has been possible due to advances. in technology especially. concerning the sensor as well as the algorithm.

References

[1]            Hosseini S A Khalilzadeh M. A & Niazmand V. (2010). Emotional stress recognition using EEG signals. IEEE Conference on Biomedical Engineering and Computer  [2]            Al-Shargie F. Tang T. B. & Kiguchi M. (2017). Assessment of mental stress using EEG and fNIRS signals. Biomedical Opt  [3]            Jenke R. Peer A. & Buss M. (2014). Feature extraction for emotion recognition from EEG. IEEE Transactions on Affective Computing.  [4]            Zheng W.L. & Lu B.L. (2015). EEGbased emotion recognition with deep neural networks. IEEE Transactions on Autonomous Mental Development.  [5]            Sulaiman N. Et al. Review of stress detection methods using EEG signals. Symmetry Journal.2019.  [6]            Shon D. Im K. & Kim J. M. (2018). Stress state detection using EEG and machine learning techniques. Applied Sciences Journal.  [7]            Sanei S. & Chambers J. A. (2013). EEG Signal Processing. John Wiley & Sons 8. Li X. & Zhang H. (2020). EEG-based mental stress detection using deep learning. Frontiers in Psychology.  [8]            OpenBCI. (2024). EEG Headset and Data Acquisition Platform.  [9]            Sulaiman N. Et al. Review of stress detection methods using EEG signals. Symmetry Journal.2019.  [10]         Shon D. Im K. & Kim J. M. (2018). Stress state detection using EEG and machine learning techniques. Applied Sciences Journal.  [11]         Sanei S. & Chambers J. A. (2013). EEG Signal Processing. John Wiley & Sons 8. Li X. & Zhang H. (2020). EEG-based mental stress detection using deep learning. Frontiers in Psychology.  [12]         OpenBCI. (2024). EEG Headset and Data Acquisition Platform.  

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Publication Date

5/18/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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