Abstract
Particle Swarm algorithm has been recently used to solve many optimal point of operation problems. It also can be modified to be used as an effective classifier, which can be done by setting its fitness function into the classification threshold. In this system, Discrete Wavelet Transform is used to extract main features in ElectroEencephaloGram (EEG) signals for moving arms and fingers in both hands. Proposed system has been implemented in FPGA with block RAMs saving the EEG dataset and designed VHDL module for wavelet transform followed by XILINX Microblaze soft core processor compiling particle swarm classifier, running on 125 MHz. The implementation results of hardware speed up the system with time of 8 ms against 30 s in software execution.The trade-off between area and latency is optimized in the proposed system, as compared with other implementations utilities, which makes the used implementation techniques and optimization better in all hardware parameters required for any biomedical applications.
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Wafeek, N., Elbably, M.E., Mubarak, R.I. (2021). FPGA Implementation of EEG Classification System for Arm and Fingers Movements Based on Particle Swarm Algorithm. In: Hassanien, A.E., et al. Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021). AICV 2021. Advances in Intelligent Systems and Computing, vol 1377. Springer, Cham. https://doi.org/10.1007/978-3-030-76346-6_31
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DOI: https://doi.org/10.1007/978-3-030-76346-6_31
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