Ayman Altameem1, Jaideep Singh Sachdev2, Vijander Singh2, Ramesh Chandra Poonia3, Sandeep Kumar4, Abdul Khader Jilani Saudagar5,*
Computer Systems Science and Engineering, Vol.42, No.3, pp. 1095-1107, 2022, DOI:10.32604/csse.2022.023256
Abstract Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We… More >
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