Abstract
Intelligent Systems for bio-signals processing and modeling are a method for creating signals to measure the brain activities to perform a task by an external device. Brain–Machine Interface (BMI) that is also known as Brain–Computer Interface (BCI), Neural Control Interface (NCI), Mind–Machine Interface (MMI), and Direct Neural Interface (DNI) is a direct communication pathway between brain and machine. Recently, computational modeling researchers are applying BMI techniques to explore advanced knowledge for discovering biological fundamental problems. In this paper, we have explored BMI techniques and developed a system that can distinguish human thoughts. Initially, we have obtained the brain signals and extracted features from these signals to build training and test data. We have designed binary-class and three-class classifiers by employing OneR, naïve Bayes (NB) classifier, decision tree (DT) induction, Random Forest, and Bagging classifiers. Random Forest achieved 93.16 and 62.84% accuracy for binary-class and three-class classification. On the contrary, decision tree (C4.5) classifier achieved 90.89 and 65.66% accuracy for binary-class and three-class classification. Then we have considered overall performance and applied decision tree classifier for developing an interactive game that can operate through brain–machine interface without physical interaction with the computer.
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Ochiuddin Miah, M., Hassan, A.M., Mamun, K.A.A., Farid, D.M. (2020). Brain–Machine Interface for Developing Virtual-Ball Movement Controlling Game. In: Uddin, M., Bansal, J. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-7564-4_51
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DOI: https://doi.org/10.1007/978-981-13-7564-4_51
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