Abstract
Researchers have been using Electroencephalography (EEG) to build Brain-Computer Interfaces (BCIs) systems. They have had a lot of success modeling brain signals for applications, including emotion detection, user identification, authentication, and control. The goal of this study is to employ EEG-based neurological brain signals to recognize imagined objects. The user imagines the object after looking at the same on the monitor screen. The EEG signal is recorded when the user thinks up about the object. These EEG signals were processed using signal processing methods, and machine learning algorithms were trained to classify the EEG signals. The study involves coarse and fine level EEG signal classification. The coarse-level classification categorizes the signals into three classes (Char, Digit, Object), whereas the fine-level classification categorizes the EEG signals into 30 classes. The recognition rates of 97.30%, and 93.64% were recorded at coarse and fine level classification, respectively. Experiments indicate the proposed work outperforms the previous methods.
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Al-Qazzaz, N.K., Hamid Bin Mohd Ali, S., Ahmad, S.A., Islam, M.S., Escudero, J.: Selection of mother wavelet functions for multi-channel EEG signal analysis during a working memory task. Sensors 15(11), 29015–29035 (2015). https://doi.org/10.3390/s151129015
Amin, H.U., Mumtaz, W., Subhani, A., Mohamad Saad, M.N., Malik, A.: Classification of EEG signals based on pattern recognition approach. Front. Comput. Neurosci. 11, 103 (2017). https://doi.org/10.3389/fncom.2017.00103
Ang, K.K., Guan, C.: EEG-based strategies to detect motor imagery for control and rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 25(4), 392–401 (2016)
Bang, J., Jeong, J.H., Won, D.O.: Classification of visual perception and imagery based EEG signals using convolutional neural networks. In: 2021 9th International Winter Conference on Brain-Computer Interface (BCI), pp. 1–6 (2021)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Carrión-Ojeda, D., Fonseca-Delgado, R., Pineda, I.: Analysis of factors that influence the performance of biometric systems based on EEG signals. Expert Syst. Appl. 165, 113967 (2021). https://doi.org/10.1016/j.eswa.2020.113967
Costa, F.R.L., Iáñez, E., Azorín, J., Patow, G.: Classify four imagined objects with EEG signals. Evol. Intel. 1–10 (2021). https://doi.org/10.1007/s12065-021-00577-y
Crasto, N., Upadhyay, R.: Wavelet decomposition based automatic sleep stage classification using EEG. In: Rojas, I., Ortuño, F. (eds.) IWBBIO 2017. LNCS, vol. 10208, pp. 508–516. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56148-6_45
Dong, H., Yang, G., Liu, F., Mo, Y., Guo, Y.: Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks. In: Valdés Hernández, M., González-Castro, V. (eds.) MIUA 2017. CCIS, vol. 723, pp. 506–517. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60964-5_44
Emotiv: EEG emotiv epoc+, https://www.emotiv.com/epoc/. Accessed 9 Feb 2020
Heckert, N., et al.: Handbook 151: Nist/sematech e-handbook of statistical methods (2002)
Kong, W., Song, X., Sun, J.: Emotion recognition based on sparse representation of phase synchronization features. Multimed. Tools. Appl. 80(14), 21203–21217 (2021)
Kumar, P., Saini, R., Roy, P.P., Sahu, P.K., Dogra, D.P.: Envisioned speech recognition using EEG sensors. Pers. Ubiquit. Comput. 22(1), 185–199 (2018)
Lee, S.H., Lee, M., Jeong, J.H., Lee, S.W.: Towards an EEG-based intuitive BCI communication system using imagined speech and visual imagery. In: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 4409–4414 (2019)
Lee, S.H., Lee, M., Lee, S.W.: Neural decoding of imagined speech and visual imagery as intuitive paradigms for BCI communication. IEEE Trans. Neural Syst. Rehabil. Eng. 28, 2647–2659 (2020)
Lu, Y., Bi, L.: Eeg signals-based longitudinal control system for a brain-controlled vehicle. IEEE Trans. Neural Syst. Rehabil. Eng. 27(2), 323–332 (2018)
MacKay, D.J.C.: Information Theory, Inference, and Learning Algorithms. Cambridge University Press, Cambridge (2003)
Maheshwari, D., Ghosh, S., Tripathy, R., Sharma, M., Acharya, U.R.: Automated accurate emotion recognition system using rhythm-specific deep convolutional neural network technique with multi-channel EEG signals. Comput. Biol. Med. 134, 104428 (2021). https://doi.org/10.1016/j.compbiomed.2021.104428
Min, B., Kim, J., Park, H., Lee, B.: Vowel imagery decoding toward silent speech BCI using extreme learning machine with electroencephalogram. BioMed Research International (2016)
Mishra, S., Birok, R.: Literature review: sleep stage classification based on EEG signals using artificial intelligence technique. Recent Trends in Communication and Electronics, pp. 241–244 (2021)
Panachakel, J.T., Ramakrishnan, A.G.: Decoding covert speech from EEG-a comprehensive review. Front. Neurosci. 15, 392 (2021)
Products, B.: actiCHamp Plus. https://www.brainproducts.com/productdetails.php?id=74. Accessed 9 Feb 2020
Rajagopal, D., Hemanth, S., Yashaswini, N., Sachin, M., Suryakanth, M.: Detection of Alzheimer’s disease using BCI. Int. J. Progressive Res. Sci. Eng. 1(4), 184–190 (2020)
Rajinikanth, V., Joseph Raj, A.N., Thanaraj, K.P., Naik, G.R.: A customized VGG19 network with concatenation of deep and handcrafted features for brain tumor detection. Appl. Sci. 10(10), 3429 (2020)
Saha, P., Fels, S.: Hierarchical deep feature learning for decoding imagined speech from EEG. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 33, pp. 10019–10020 (2019)
Saini, R., et al.: Don’t just sign use brain too: a novel multimodal approach for user identification and verification. Inf. Sci. 430, 163–178 (2018)
Santaji, S., Santaji, S., Desai, V.: Automatic sleep stage classification with reduced epoch of EEG. Evolutionary Intelligence, pp. 1–8 (2021)
Simpraga, S., et al.: EEG machine learning for accurate detection of cholinergic intervention and Alzheimer’s disease. Sci. Rep. 7(1), 1–11 (2017)
Vecchio, F., et al.: Classification of Alzheimer’s disease with respect to physiological aging with innovative EEG biomarkers in a machine learning implementation. J. Alzheimers Dis. 75(4), 1253–1261 (2020)
Vidaurre, C., Klauer, C., Schauer, T., Ramos-Murguialday, A., Müller, K.R.: EEG-based BCI for the linear control of an upper-limb neuroprosthesis. Med. Eng. Phys. 38(11), 1195–1204 (2016)
Yang, S., Deravi, F.: On the usability of electroencephalographic signals for biometric recognition: a survey. IEEE Trans. Hum.-Mach. Syst. 47(6), 958–969 (2017)
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Saini, R. et al. (2022). Imagined Object Recognition Using EEG-Based Neurological Brain Signals. In: Santosh, K., Hegadi, R., Pal, U. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2021. Communications in Computer and Information Science, vol 1576. Springer, Cham. https://doi.org/10.1007/978-3-031-07005-1_26
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DOI: https://doi.org/10.1007/978-3-031-07005-1_26
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