Autors: Neshov, N. N., Manolova, A. H., Draganov, I. R., Tonchev, K.T., Boumbarov, O.L.
Title: Classification of Mental Tasks from EEG Signals using Spectral Analysis, PCA and SVM
Keywords: Electroencephalography (EEG); Brain Computer Interface (BCI); Fast Fourier Transform (FFT); Principal component analysis (PCA); Support vector machine (SVM)

Abstract: Signals provided by the Electroencephalography (EEG) are widely used in brain-computer interface (BCI) applications. They can be further analyzed and used for thinking activity recognition. In this paper we proposed an algorithm that is able to recognize five mental tasks using 6 channel EEG data. The main idea is to separate the raw EEG signals into several frames and compute their spectrums. Next, a second-order derivative of Gaussian is applied to extract features and an optimum Gaussian kernel parameters grid search is performed with the help of cross-validation. The extracted features are further reduced by Principal Component Analysis. The processed data is utilized to train SVM classifier which is used for mental tasks recognition afterwards. The performance of the algorithm is estimated on publically available dataset. In terms of 5 folds cross-validation we obtained an average of 82.7% recognition rate (accuracy).



    Cybernetics and Information Technologies, vol. 18, issue 1, pp. 81-92, 2018, Bulgaria, Bulgarian Academy of Sciences, DOI 10.2478/cait-2018-0007

    Copyright Sciendo

    Цитирания (Citation/s):
    1. Lohani, M., Payne, B.R., Strayer, D.L., A review of psychophysiological measures to assess cognitive states in real-world driving, Frontiers in Human Neuroscience 13,57 - 2019 - в издания, индексирани в Scopus или Web of Science
    2. Dong, N., Li, Y., Gao, Z., Ip, W.H., Yung, K.L., A WPCA-based method for detecting fatigue driving from EEG-based internet of vehicles system, IEEE Access 7,8815722, pp. 124702-124711 - 2019 - в издания, индексирани в Scopus или Web of Science
    3. Jiang, H., Wang, Z., Jiao, R., Jiang, S., Picture-induced EEG signal classification based on CVC emotion recognition system, Computers, Materials and Continua 65(2), pp. 1453-1465 - 2020 - в издания, индексирани в Scopus или Web of Science
    4. A. Gupta et al., "On the Utility of Power Spectral Techniques With Feature Selection Techniques for Effective Mental Task Classification in Noninvasive BCI," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, doi: 10.1109/TSMC.2019.2917599. - 2021 - в издания, индексирани в Scopus или Web of Science
    5. M. S. Murtazina and T. V. Avdeenko, "Classification of Brain Activity Patterns Using Machine Learning Based on EEG Data," 2020 1st International Conference Problems of Informatics, Electronics, and Radio Engineering (PIERE), 2020, pp. 219-224, doi: 10.1109/PIERE51041.2020.9314660. - 2020 - в издания, индексирани в Scopus или Web of Science
    6. Han, L., Lu, L., Dong, H., Xie, S., Yu, G., Shen, T., Sun, M., Wang, T., Pei, X., Feature Extraction Method of EEG Signal Based on Synchroextracting Transform, In Proc. of 3rd EAI International Conference on Multimedia Technology and Enhanced Learning, ICMTEL 2021, April 8-9, Virtual event, In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Vol. 388, pp. 462-468, Springer, 2021, DOI 10.1007/978-3-030-82565-2_38 - 2021 - в издания, индексирани в Scopus или Web of Science
    7. P. Mathur and V. K. Chakka, "Graph Signal Processing Based Cross-Subject Mental Task Classification Using Multi-Channel EEG Signals," in IEEE Sensors Journal, doi: 10.1109/JSEN.2022.3156152. - 2022 - в издания, индексирани в Scopus или Web of Science

    Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science