Optimizing Brain Signal Classification through Computational Neuroscience with EMOTIV and Free Software
DOI:
https://doi.org/10.61467/2007.1558.2026.v17i2.1264Keywords:
Neurotechnology, Computational NeuroscienceAbstract
This study presents a method for the acquisition, preprocessing, and classification of brain signals aimed at brain mapping and the development of a brain-computer interface (BCI) using non-invasive electroencephalography (EEG). The proposed system is based on the hypothesis that imagined motor actions can be translated into control commands to displace a robotic prototype in four basic directions: right, left, push, and pull. For this purpose, raw biosignals were collected with EMOTIV headsets (EPOC X and Insight), exported in .csv and .edf formats, and subjected to a preprocessing pipeline including artifact removal, digital filtering (1–40 Hz), and statistical outlier detection to ensure data quality.
The signals were then analyzed with different classification methods (J48 Decision Tree, RandomForest, NaiveBayes, and Support Vector Machines) to identify the most suitable model for BCI-based neurocontrol. Results show that RandomForest achieved the highest accuracy, reaching 100% with external test data, followed closely by J48 with 99.01%, while NaiveBayes and SVM demonstrated limited reliability under cross-validation.
Finally, the methodology was validated through the neurocontrol of a robotic prototype using Arduino hardware, where participants successfully executed directional commands via mental activity. These findings demonstrate the feasibility of integrating consumer-grade EEG devices, free software, and machine learning techniques to develop robust and accessible BCI systems for assistive robotics. The contribution of this work lies in optimizing mental command recognition and highlighting the potential of EEG-based neurocontrol technologies to enhance mobility and autonomy in individuals with motor disabilities.
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References
United Nations Sustainable Development Group. (2021, November). UN DCO disability inclusion policy 2022–2025. https://unsdg.un.org/resources/un-dco-disability-inclusion-strategy-2022-2025
Cheng, N., Phua, K. S., Lai, H. S., Tam, P. K., & Tang, K. Y. (2020). Brain–computer interface–based soft robotic glove rehabilitation for stroke. IEEE Transactions on Biomedical Engineering, 67(12), 3339–3351. https://doi.org/10.1109/TBME.2020.2990454
Kumar, A., Gao, L., Pirogova, E., & Fang, Q. (2019). A review of error-related potential–based brain–computer interfaces for motor-impaired people. IEEE Access, 7, 142451–142466. https://doi.org/10.1109/ACCESS.2019.2944584
Instituto Nacional de Estadística y Geografía. (2020). Censo de población y vivienda 2020. http://cuentame.inegi.org.mx/poblacion/discapacidad.aspx
Hooda, N., Das, R., & Kumar, N. (2020). Cognitive control of robotic-rehabilitation device using Emotiv EEG headset. En Proceedings of the International Conference on Computational Science and Its Applications (pp. 57–65). Springer. https://doi.org/10.1007/978-3-030-58808-3_5
De Pace, F., Manuri, F., Bosco, M., Sanna, A., & Kaufmann, H. (2024). Supporting human–robot interaction by projected augmented reality and a brain interface. IEEE Transactions on Human-Machine Systems. https://doi.org/10.1109/THMS.2024.3360193
Chaudhary, U., Xia, B., Silvoni, S., Cohen, L. G., & Birbaumer, N. (2017). Brain–computer interface–based communication in the completely locked-in state. PLOS Biology, 15(1), e1002593. https://doi.org/10.1371/journal.pbio.1002593
Lira Díaz, O., Huerta Becerra, A. O., Gómez Bautista, D. J., & Carrillo Cisneros, A. E. (2023). Diseño e implementación de un entrenador de prótesis electroencefalográficas usando la diadema Emotiv Epoc+. Ciencia Latina, 7(6).
Sánchez González, D., Barragán, J., Mendoza Montoya, O., & Antelis, J. M. (2023). Análisis y clasificación de señales electroencefalográficas para el control de una ortesis robótica de mano basada en imaginación motora. Research in Computing Science, 152(9), 217–231.
León Sánchez, C. N., Benavides Álvarez, C., Avilés Cruz, C., Zúñiga López, A., & Celis Escudero, G. J. (2024). Robotic hand control system by means of a Raspberry Pi and a Python interface. Pistas Educativas, 45(147).
Muraskin, J., Sherwin, J., Lieberman, G., Garcia, J. O., & Verstynen, T. (2017). Fusing multiple neuroimaging modalities to assess group differences in perception–action coupling. Proceedings of the IEEE, 106(1), 83–100. https://doi.org/10.1109/JPROC.2017.2765100
Sheng, Y., Chen, J., He, X., Xu, Z., & Gao, J. (2020). A topic learning pipeline for curating brain cognitive researches. IEEE Access, 8, 191758–191774. https://doi.org/10.1109/ACCESS.2020.3032648
Makeig, S., Gramann, K., Jung, T.-P., Sejnowski, T. J., & Poizner, H. (2009). Linking brain, mind and behavior. International Journal of Psychophysiology, 73(2), 95–100. https://doi.org/10.1016/j.ijpsycho.2008.11.008
Freire Carrera, F., Chadrina, O., Maila Andrango, E., & Drozdov, V. (2019). Diseño de sistema para controlar una silla de ruedas mediante señales eléctricas cerebrales. MediSur, 17(5), 650–663.
Rebsamen, B., Guan, C., Zhang, H., Wang, C., Teo, C., Ang, M. H., & Burdet, E. (2010). A brain controlled wheelchair to navigate in familiar environments. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 18(6), 590–598. https://doi.org/10.1109/TNSRE.2010.2049862
Al Rakib, M. A., Uddin, S., & Rahman, M. M. (2021). Smart wheelchair with voice control for physically challenged people. European Journal of Engineering and Technology Research.
Aly, A. A., et al. (2022). Adaptive neural network-based fixed-time tracking controller for disabilities exoskeleton wheelchair robotic system. Mathematics. https://doi.org/10.3390/math10010127
EMOTIV. (2025). EMOTIV. https://www.emotiv.com/
Cortés Barrera, G., Ruiz Garduño, J. K., González Ramírez, C. T., Sossa Azuela, J. H., & Jiménez Alfaro, A. J. (2025). Computational neuroscience using EMOTIV tools and free software. Abstraction & Application, 49, 77–90.
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