2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 21 , Issue 2 , PP: 327-335, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Abdulrahman W. H. Al-Askari 1 *
Doi: https://doi.org/10.54216/FPA.210220
In recent years, EEG based recognition and characterization of brain states has received much interest due to the advances in deep learning and machine learning methods. The non-invasive and highly inexpensive activity of EEG presents a patient with details concerning the activity and the conditions of the brain. The synthesis of artificial intelligence (AI) models (convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and collaborative knowledge options has been explored in a series of studies that recognize the mental state accurately in a large number of cases. The literature focuses on introducing strong, explainable models as well as on multimodal data to boost classification accurateness and reliability. The results are a 1D CNN and a LSTM network were trained separately and in a hybrid, architecture (CNN-LSTM) to classify the EEG signals. The models were appraised using accurateness, accuracy, recollection, F1-score, and confusion matrix analysis.
EEG , Deep Learning , Mental State Classification , CNN-LSTM , Brain-Computer Interface
[1] J. Ruiz De Miras, A. J. Ibáñez-Molina, M. F. Soriano, and S. Iglesias-Parro, “Schizophrenia classification using machine learning on resting state EEG signal,” Biomed. Signal Process. Control, vol. 79, Jan. 2023, Art. no. 104233. doi: 10.1016/j.bspc.2022.104233.
[2] J. Teo, L. Hou, J. Tian, and J. Mountstephens, “Classification of affective states via EEG and deep learning,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 5, 2018. doi: 10.14569/IJACSA.2018.090517.
[3] A. Rahman, M. I. Siraji, L. I. Khalid et al., “Detection of mental state from EEG signal data: An investigation with machine learning classifiers,” in 2022 14th Int. Conf. Knowl. Smart Technol. (KST), Jan. 2022, pp. 152–156. doi: 10.1109/KST53302.2022.9729084.
[4] P. Bashivan, I. Rish, and S. Heisig, “Mental state recognition via wearable EEG,” arXiv: 1602.00985, Jun. 5, 2016. doi: 10.48550/arXiv.1602.00985.
[5] SEED Dataset. [Online]. Available: https://bcmi.sjtu.edu.cn/home/seed/
[6] H. Masruroh, E. M. Imah, and E. Rahmawati, “Classification of emotional state based on EEG signal using AMGLVQ,” Procedia Comput. Sci., vol. 157, pp. 552–559, 2019. doi: 10.1016/j.procs.2019.09.013.
[7] O. AlShorman, M. Masadeh, M. B. B. Heyat et al., “Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection,” J. Integr. Neurosci, vol. 21, no. 1, p. 20, 2022. doi: 10.31083/j.jin2101020.
[8] W. Ma, Y. Zheng, T. Li, Z. Li, Y. Li, and L. Wang, “A comprehensive review of deep learning in EEG-based emotion recognition: Classifications, trends, and practical implications,” PeerJ Comput. Sci., vol. 10, p. e2065, May 2024. doi: 10.7717/peerj-cs.2065.
[9] S. Rasheed, “A review of the role of machine learning techniques towards brain–computer interface applications,” Mach. Learn. Knowl. Extr., vol. 3, no. 4, pp. 835–862, 2021. doi: 10.3390/make3040042.
[10] Appriou, A. Cichocki, and F. Lotte, “Modern machine-learning algorithms: For classifying cognitive and affective states from electroencephalography signals,” IEEE Syst., Man, Cybern. Mag., vol. 6, no. 3, pp. 29–38, 2020. doi: 10.1109/MSMC.2020.2968638.
[11] M. J. Rivera, M. A. Teruel, A. Maté, and J. Trujillo, “Diagnosis and prognosis of mental disorders by means of EEG and deep learning: A systematic mapping study,” Artif. Intell. Rev., vol. 55, no. 2, pp. 1209–1251, 2022. doi: 10.1007/s10462-021-09986-y.
[12] D. R. Edla, K. Mangalorekar, G. Dhavalikar, and S. Dodia, “Classification of EEG data for human mental state analysis using random forest classifier,” Procedia Comput. Sci., vol. 132, pp. 1523–1532, 2018. doi: 10.1016/j.procs.2018.05.116.
[13] S.-Y. Han, N.-S. Kwak, T. Oh, and S.-W. Lee, “Classification of pilots’ mental states using a multimodal deep learning network,” Biocybern. Biomed. Eng., vol. 40, no. 1, pp. 324–336, 2020. doi: 10.1016/j.bbe.2019.12.002.
[14] Z. Jiao, X. Gao, Y. Wang, J. Li, and H. Xu, “Deep convolutional neural networks for mental load classification based on EEG data,” Pattern Recognit., vol. 76, pp. 582–595, Apr. 2018. doi: 10.1016/j.patcog.2017.12.002.
[15] S. Sridhar and V. Manian, “EEG and deep learning based brain cognitive function classification,” Computers, vol. 9, no. 4, p. 104, 2020. doi: 10.3390/computers9040104.
[16] K. Ajith, R. Menaka, and S. S. Kumar, “EEG based mental state analysis,” J. Phys., Conf. Ser., vol. 1911, no. 1, p. 012014, 2021. doi: 10.1088/1742-6596/1911/1/012014.
[17] Y. Badr, U. Tariq, F. Al-Shargie, F. Babiloni, F. Al Mughairbi, and H. Al-Nashash, “A review on evaluating mental stress by deep learning using EEG signals,” Neural Comput. Appl., vol. 36, no. 21, pp. 12629–12654, 2024. doi: 10.1007/s00521-024-09809-5.
[18] R. Alhalaseh and S. Alasasfeh, “Machine-learning-based emotion recognition system using EEG signals,” Computers, vol. 9, no. 4, p. 95, 2020. doi: 10.3390/computers9040095.
[19] H. Manoharan, S. L. A. Haleem, S. Shitharth et al., “A machine learning algorithm for classification of mental tasks,” Comput. Electr. Eng., vol. 99, Apr. 2022, Art. no. 107785. doi: 10.1016/j.compeleceng.2022.107785.
[20] H. Dose, J. S. Møller, H. K. Iversen, and S. Puthusserypady, “An end-to-end deep learning approach to MI-EEG signal classification for BCIs,” Expert Syst. Appl., vol. 114, pp. 532–542, Dec. 2018. doi: 10.1016/j.eswa.2018.08.031.
[21] R. Katmah, F. Al-Shargie, U. Tariq, F. Babiloni, F. Al-Mughairbi, and H. Al-Nashash, “A review on mental stress assessment methods using EEG signals,” Sensors, vol. 21, no. 15, p. 5043, 2021. doi: 10.3390/s21155043.
[22] Y.-T. Lan, K. Ren, Y. Wang et al., “Seeing through the brain: Image reconstruction of visual perception from human brain signals,” arXiv: 2308.02510, Aug. 16, 2023. doi: 10.48550/arXiv.2308.02510.
[23] C. Anders and B. Arnrich, “Wearable electroencephalography and multi-modal mental state classification: A systematic literature review,” Comput. Biol. Med., vol. 150, Nov. 2022, Art. no. 106088. doi: 10.1016/j.compbiomed.2022.106088.
[24] R. Nahon, N. Bagheri, G. Varni, E. Tartaglione, and V.-T. Nguyen, “Mental state classification using EEG signals: Ethics, law and challenges,” in Mach. Learn. Princ. Pract. Knowl. Discov. Databases, R. Meo and F. Silvestri, Eds., vol. 2133. Cham, Switzerland: Springer Nature, 2025. doi: 10.1007/978-3-031-74630-7_28.
[25] D. Pathak, R. Kashyap, and S. Rahamatkar, “A study of deep learning approach for the classification of electroencephalogram (EEG) brain signals,” in Artif. Intell. Mach. Learn. Edge Comput. Elsevier, 2022. doi: 10.1016/B978-0-12-824054-0.00009-5.
[26] Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: A review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. doi: 10.1088/1741-2552/ab0ab5.
[27] M. A. Ozdemir, M. Degirmenci, O. Guren, and A. Akan, “EEG based emotional state estimation using 2-D deep learning technique,” in 2019 Med. Technol. Congr. (TIPTEKNO), Oct. 2019, pp. 1–4. doi: 10.1109/TIPTEKNO.2019.8895158.
