Abstract
This research shows the performance of a multilayer perceptron (MLP) neural network in the classification of electroencephalographic (EEG) signals, for which the Emotiv Insight headset is used for the signal acquisition stage in order to generate data from EEG recordings, where two types of brain patterns were previously selected for this stage: left winks and right winks; so that through preprocessing stages, feature extraction, the feature matrix of the collected data is generated and thus form the input patterns to be used by the neural network in its training process. The main objective of the research is to make use of the classification model generated as a result of the neural network training in OFF LINE mode, thus an application has been designed in which this model is tested to evaluate if the outputs it emits correspond to the type of recording that is delivered as input. Therefore, the precision performance of the model during the testing phase was 90%, thus, the model can be considered to work in an application that requires to be covered in ON LINE mode, for example, in the so-called BCI systems (Brain Computer Interface), that translate the user's brain activity into control commands.
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References
Ortiz-Rodriguez, J.M., et al.: A study using the robust design of artificial neural networks methodology in neutron spectrometry. In: Proc. IEEE Int. Conf. Ind. Technol, pp. 1600–1606 (2016)
Singh, J., Dhiman, G.: A survey on machine-learning approaches: theory and their concepts. Materials Today: Proc. (2021). https://doi.org/10.1016/j.matpr.2021.05.335
Tang, C., Wang, Z., Sima, X., Zhang, L.: Research on artificial intelligence algorithm and its application in games. In: Proc. - 2020 2nd Int. Conf. Artif. Intell. Adv. Manuf. AIAM, pp. 386–389 (2020)
Guambo, W., et al.: Performance Evaluation of a Predictive and PID Control Applied to a Didactic Flow Regulation Process. In: Intelligent Manufacturing and Energy Sustainability. Springer, Singapore, pp. 559–570 (2022)
Tiwari, N., et al.: Brain computer interface: a comprehensive survey. Biologically Inspired Cognitive Architectures 26, 118–129 (2018)
Ray, S.: A quick review of machine learning algorithms. In: Proc. Int. Conf. Mach. Learn. Big Data, Cloud Parallel Comput. Trends, Prespectives Prospect. Com (2019) 35–39
Henríquez, C.: Estudio de Técnicas de análisis y clasificación de señales EEG en el contexto de Sistemas BCI (Brain Computer Interface), Master. thesis, Universidad Autónoma de Madrid, Madrid, España, March 2014
Shen, M., Zhang, X., Li, X.: Independent component analysis of electroencephalographic signals. In: International Conference Signal Process. Proceedings, ICSP, pp. 1548–1551 (2002)
Bermúdez, A.: Técnicas de procesamiento de EEG para detección de eventos, Master. thesis, Universidad Nacional de la Plata, La Plata, Argentina, Aug. (2013)
Kim, J.H., Bießmann, F., Lee, S.W.: Reconstruction of hand movements from EEG signals based on non-linear regression. In: 2014 Int. Winter Work. Brain-Computer Interface, pp. 3–5 (2014)
Kam, T.E., Il Suk, H., Lee, S.W.: Non-homogeneous spatial filter optimization for EEG-based brain-computer interfaces. In: 2013 International Winter Work. Brain-Computer Interface, pp. 26–28 (2013)
Lee, S., Shin, Y., Woo, S., Kim, K., Lee, H.: Dry electrode design and performance evaluation for EEG based Bel systems. In: 2013 Int. Winter Work. Brain-Computer Interface, pp. 52–53 (2013)
Damalerio, R., Cheng, M.Y.: Development of Dry EEG Electrodes and Dry EEG Cap for Neuromonitoring. In: Proc. - Electron. Components Technol. Conf, pp. 841–846 (2020)
Ramadan, R.A., Vasilakos, A.V.: Brain computer interface: control signals review. Neurocomputing, 26–44 (2017)
Emotiv Inc. website. [Online 2021]. https://emotiv.gitbook.io/insight-manual/
Sebastián, J., Silva, O.: Caracterización de gestos faciales y oculares mediante EEG utilizando SVM, M. Eng. Thesis, Fundación Universitaria Los Libertadores, Bogotá, Colombia (2018)
Harke Pratama, S., et al.: Signal Comparison of Developed EEG Device and Emotiv Insight Based on Brainwave Characteristics Analysis. J. Phys. Conf. Ser. (2020). https://doi.org/10.1088/1742-6596/1505/1/012071
Vacacela, S.G., Freire, L.O.: Implementation of a network of wireless weather stations using a protocol stack. In: Reddy, A., Marla, D., Favorskaya, M.N., Satapathy, S.C. (eds.) Intelligent Manufacturing and Energy Sustainability, vol. 213, pp. 509–517. Springer, Singapore (2021). https://doi.org/10.1007/978-981-33-4443-3_49
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Corrales, E., Corrales, B.P., Freire, L.O., Benalcázar, M.J. (2023). Artificial Neural Networks and Their Application in EEG Signal Classification. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2023. Lecture Notes in Networks and Systems, vol 669. Springer, Cham. https://doi.org/10.1007/978-3-031-29860-8_95
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DOI: https://doi.org/10.1007/978-3-031-29860-8_95
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