Abstract
Brain–machine interface (BMI) is an interfacing mechanism which enables us to interact with an external device just by our thoughts. This technique is currently in wide use in healthcare applications such as artificial limbs. And there are increasing progressions in the development of more accurate, lesser invasive technologies for brain mapping. This makes it possible to blend smart computing methods with BMI to enhance the functionality of smart gadgets like smartphones, VR and intelligent systems with highly improved responsiveness. This technique can also help people with impaired vision, hearing, speech, etc., to use and interact with such devices. This paper has overviewed the methods to implement EEG method of brain–machine interface with cloud computing and machine learning. It has also been discussed that how it can be used with smart computing and devices and how it can extend the usability of contemporary applications.
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Verma, A., Tripathi, A., Choudhury, T. (2020). Implementing Brain–Machine Interface (BMI) with Smart Computing. In: Das, A., Nayak, J., Naik, B., Dutta, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. Advances in Intelligent Systems and Computing, vol 1120. Springer, Singapore. https://doi.org/10.1007/978-981-15-2449-3_5
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DOI: https://doi.org/10.1007/978-981-15-2449-3_5
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