An Ultralow-Power Real-Time Machine Learning Based Fnirs Motion Artifacts Detection
IEEE Transactions on Very Large Scale Integration (VLSI) Systems（2024）
Key words
Support vector machines,Functional near-infrared spectroscopy,Motion artifacts,Field programmable gate arrays,Hardware,Machine learning,Kernel,Field-programmable gate array (FPGA),functional near-infrared spectroscopy (fNIRS),low power,machine learning,motion artifact detection,real time,support vector machines (SVMs)
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