Abstract
There are various stages in human mental development. Among them are consciousness, drowsiness, and light sleep. These human mental stages and conditions can be affected by human emotions (Ali et al. in Wirel Pers Commun 125:3699–3713, 2022; Katmah et al. in Sensors 21(15):5043). Hence, human brainwaves or electroencephalogram (EEG) signals can be employed to analyze and interpret the development of human mental stage. In this research, 1-channel EEG device is employed to measure neural electrical activity from five people as they are engaged in three different cognitive exercises such as playing a video game, reading a book, and watching a movie. EEG signals are analyzed in LabVIEW software to reveal the unique features which are able to describe various human stages. The EEG power spectrum in terms of mean and standard deviation for each EEG frequency band (theta band, alpha band, and beta band) is computed. Then, the k-nearest neighbor (k-NN) classifier is employed to discover the best feature that is capable to indicate status of human mental stage. The findings of the study demonstrated that the mean EEG feature with the training and testing ratio of k-NN classifier at 80:20 could detect and categorize human stages with the classification accuracy of 81.57%. Meanwhile, LabVIEW graphical user interface (GUI) and block diagram are constructed to display the analyses of human stages of each subject for the specified human stage activities. In addition, a device is built to indicate human mental stage in an off-line manner.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ali A, Afridi R, Soomro TA, Khan SA, Khan MYA, Chowdhry BS (2022) A single-channel wireless EEG headset enabled neural activities analysis for mental healthcare applications. Wirel Pers Commun 125(4):3699–3713
Katmah R, Al-Shargie F, Tariq U, Babiloni F, Al-Mughairbi F, Al-Nashash H (2021) A review on mental stress assessment methods using EEG signals. Sensors 21(15):5043
Mohanty P, Siddharth P, Swain KB, Patnaik RK (2017) Driver assistant for the detection of drowsiness and alcohol effect. In: 2017 IEEE 3rd international conference on sensing, signal processing and security (ICSSS), pp 279–283
Thornton MA, Weaverdyck ME, Tamir DI (2019) The brain represents people as the mental states they habitually experience. Nat Commun 10(1)
Georgiev DD, Georgieva I, Gong Z, Nanjappan V, Georgiev GV (2021) Virtual reality for neurorehabilitation and cognitive enhancement. Brain Sci 11(2):1–20
Rahman NAA, Mustafa M, Samad R, Abdullah NRH, Sulaiman N (2019) Energy spectral density analysis of muscle fatigue. In: 10th national technical seminar on underwater system technology (NuSYS2018), pp 437–446
Suhaimi NS, Mountstephens J, Teo J (2020) EEG-based emotion recognition: a state-of-the-art-review of current trends and opportunities. Comput Intell Neurosci 1–19
Sulaiman N, Beh SY, Mustafa M, Jadin MS (2018) Offline LabVIEW-based EEG signals analysis for human stress monitoring. In: 9th IEEE control and system graduate research colloquium (ICSGRC2018), pp 126–131
Sahu S, Sharma A (2016) Detecting brainwaves to evaluate mental health using LabVIEW and applications. In: IEEE international conference on emerging technological trends in computing, communications and electrical engineering (ICETT), India
Wadekar RS, Kasambe PV, Rathod SS (2017) Development of LabVIEW platform for EEG signal analysis. In: International conference on intelligent computing and control (I2C2), pp 225–228
Manjusha M, Harikumar R (2016) Performance analysis of KNN classifier and K-means clustering for robust classification of epilepsy from EEG signals. In: IEEE international conference on wireless communications, signal processing and networking (WiSPNET), pp 2412–2416
Rashid M, Mustafa M, Abdullah NRH, Samad R (2021) Random subspace k-NN based ensemble classifier for driver fatigue detection utilizing selected EEG channels. Traitement du Signal 38(5):1259–1270
Bablani A, Rdla DR, Dodia S (2018) Classification of EEG data using k-nearest neighbor approach for concealed information test. Proc Comput Sci 143:242–249
Acknowledgements
The author would like to appreciate the marvelous support given by the research team members, postgraduate and undergraduate students, laboratory facilities, faculty and financial support provided by university, Universiti Malaysia Pahang Al-Sultan Abdullah under research grant, RDU210318.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sulaiman, N., Mustafa, M., Samsuri, F., Aris, S.A.M., Zailani, N.I.A.M. (2024). Human Mental Stage Interpretation Based on the Analysis of Electroencephalogram (EEG) Signals. In: Mohd. Isa, W.H., Khairuddin, I.M., Mohd. Razman, M.A., Saruchi, S.'., Teh, SH., Liu, P. (eds) Intelligent Manufacturing and Mechatronics. iM3F 2023. Lecture Notes in Networks and Systems, vol 850. Springer, Singapore. https://doi.org/10.1007/978-981-99-8819-8_18
Download citation
DOI: https://doi.org/10.1007/978-981-99-8819-8_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8818-1
Online ISBN: 978-981-99-8819-8
eBook Packages: EngineeringEngineering (R0)