AccScience Publishing / BH / Online First / DOI: 10.36922/bh.2819

Recognition predictive modeling using electroencephalogram

S.K.B. Sangeetha1 Sandeep Kumar Mathivanan2 Saurav Mallik3,4* Aimin Li5
Show Less
1 Department of Computer Science and Engineering, Faculty of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India
2 Department of Computer Science and Engineering, School of Computer Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India
3 Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
4 Department of Pharmacology and Toxicology, The University of Arizona, Tucson, Arizona, United States of America
5 School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, Shaanxi, China
Brain & Heart 2024, 2(2), 2819
Submitted: 24 January 2024 | Accepted: 19 March 2024 | Published: 15 May 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( )

A machine learning model that operates on raw electroencephalogram (EEG) signals is essential for accurately discerning the user’s current thoughts. Given the difficulty of categorizing EEG signals for use in brain-computer interface (BCI) programs, we adopted a systematic approach in this study to select an optimal predictive model. To enhance the effectiveness of our systematic approach, we extracted features such as band powers, averages, and root-mean-squared values. K-nearest neighbor (KNN), principal component analysis, and dual-layer neural networks were employed to evaluate and validate the effectiveness of the extracted features. The BCI IV competition-I dataset was utilized for analysis and validation. KNN achieved an average classification success rate of 98.02% compared to other methods. Furthermore, our research extends the application of this approach using it to create, test, and evaluate human driving behavior as a case study.

Brain-computer interface
Feature extraction
Human-computer interface
Machine learning
Neural network
  1. Qi G, Zhao S, Ceder AA, Guan W, Yan X. Wielding and evaluating the removal composition of common artefacts in EEG signals for driving behaviour analysis. Accid Anal Prev. 2021;159:106223. doi: 10.1016/j.aap.2021.106223


  1. Zero E, Bersani C, Sacile R. EEG based BCI system for driver’s arm movements identification. In: Automation, Robotics and Communications for Industry 4.0. Vol. 77. France: International Frequency Sensor Association; 2021.


  1. Cao Z, Chuang CH, King JK, Lin CT. Multi-channel EEG recordings during a sustained-attention driving task. Sci Data. 2019;6(1):19. doi: 10.1038/s41597-019-0027-4


  1. Zhang X, Li J, Liu Y, et al. Design of a fatigue detection system for high-speed trains based on driver vigilance using a wireless wearable EEG. Sensors (Basel). 2017;17(3):486. doi: 10.3390/s17030486


  1. Nader M, Jacyna-Gołda I, Nader S, Nehring K. Using BCI and EEG to process and analyze driver’s brain activity signals during VR simulation. Arch Transp. 2021;60:137-153. doi: 10.5604/01.3001.0015.6305


  1. Zhou X, Yao D, Zhu, M, et al. Vigilance detection method for high‐speed rail using wireless wearable EEG collection technology based on low‐rank matrix decomposition. IET Intell Transp Syst. 2018;12(8):819-825. doi: 10.1049/iet-its.2017.0239


  1. He S, Chen L, Yue M. Reliability analysis of driving behaviour in road traffic system considering synchronization of neural activity. NeuroQuantology. 2018;16(4):62-68. doi: 10.14704/nq.2018.16.4.1209


  1. Lawhern VJ, Solon AJ, Waytowich NR, Gordon SM, Hung CP, Lance BJ. EEGNet: A compact convolutional neural network for EEG-based brain-computer interfaces. J Neural Eng. 2018;15:056013. doi: 10.1088/1741-2552/aace8c


  1. Doudou M, Bouabdallah A, Berge-Cherfaoui V. Driver drowsiness measurement technologies: Current research, market solutions, and challenges. Int J Intell Transp Syst Res. 2020;18(2):297-319. doi: 10.1007/s13177-019-00199-w


  1. Haghani M, Bliemer MC, Farooq B, et al. Applications of brain imaging methods in driving behaviour research. Accid Anal Prev. 2021;154:106093. doi: 10.1016/j.aap.2021.106093


  1. Murthy GN, Khan ZA. Cognitive attention behaviour detection systems using Electroencephalograph (EEG) signals. Res J Pharm Technol. 2014;7(2):238-247.


  1. Pal D, Palit S, Dey A. Brain computer interface: A review. In: Computational Advancement in Communication, Circuits and Systems. Cham: Springer; 2022. p. 25-35. doi: 10.1007/978-3-319-10978-7_1


  1. Zero E, Bersani C, Zero L, Sacile R. Towards real-time monitoring of fear in driving sessions. IFAC-PapersOnLine. 2019;52(19):299-304. doi: 10.1016/j.ifacol.2019.12.068


  1. Aricò P, Borghini G, Di Flumeri G, Sciaraffa N, Babiloni F. Passive BCI beyond the lab: Current trends and future directions. Physiol Meas. 2018;39(8):08TR02.doi: 10.1088/1361-6579/aad57e


  1. Hasan MJ, Shon D, Im K, Choi HK, Yoo DS, Kim JM. Sleep state classification using power spectral density and residual neural network with multichannel EEG signals. Appl Sci. 2020;10:7639. doi: 10.3390/app10217639


  1. Brouwer AM, Snelting A, Jaswa M, Flascher O, Krol L, Zander T. Physiological Effects of Adaptive Cruise Control Behaviour in Real Driving. In: Proceedings of the 2017 ACM Workshop on an Application-oriented Approach to BCI out of the Laboratory. 2017. p. 15-19. doi: 10.1145/3038439.3038441


  1. Karuppusamy NS, Kang BY. Multimodal system to detect driver fatigue using EEG, gyroscope, and image processing. IEEE Access. 2020;8:129645-129667. doi: 10.1109/Access.2020.3009226


  1. Sangeetha SKB, Kumar MS, Deeba K, Rajadurai H, Maheshwari V, Dalu GT. An empirical analysis of an optimized pretrained deep learning model for COVID-19 diagnosis. Comput Math Methods Med. 2022;2022:9771212. doi: 10.1155/2022/9771212


  1. Khalaf OI, Ogudo KA, Sangeetha SKB. Design of Graph-based layered learning-driven model for anomaly detection in distributed cloud IoT network. Mob Inf Syst. 2022;2022:6750757. doi: 10.1155/2022/6750757


  1. Kanthavel D, Sangeetha SKB, Keerthana KP. An empirical study of vehicle to infrastructure communications-an intense learning of smart infrastructure for safety and mobility. Int J Intell Netw. 2021;2:77-82. doi: 10.1016/j.ijin.2021.06.003


  1. Aggarwal S, Chugh N. Review of machine learning techniques for EEG based brain computer interface. Arch Comput Methods Eng. 2022;29:3001-3020. doi: 10.1007/s11831-021-09684-6


  1. Ahn M, Jun SC, Yeom HG, Cho H. Editorial: Deep learning in brain-computer interface. Front Hum Neurosci. 2022;16:927567. doi: 10.3389/fnhum.2022.927567


  1. Zhu H, Forenzo D, He B. On the deep learning models for EEG-based brain-computer interface using motor imagery. IEEE Trans Neural Syst Rehabil Eng. 2022;30:2283-2291. doi: 10.1109/TNSRE.2022.3198041


  1. Immanuel RR, Sangeetha SKB. Analysis of EEG Signal with Feature and Feature Extraction Techniques for Emotion Recognition Using Deep Learning Techniques. In: Proceedings of International Conference on Computational Intelligence and Data Engineering. Singapore: Springer Nature Singapore; 2022. p. 141-154. doi: 10.1007/978-981-99-0609-3_10
Conflict of interest
The authors declare that they have no competing interests.
Back to top
Brain & Heart, Electronic ISSN: 2972-4139 Published by AccScience Publishing