https://www.selleckchem.com/products/gdc-0068.html
The recent progress in recognizing low-resolution instantaneous high-density surface electromyography (HD-sEMG) images opens up new avenues for the development of more fluid and natural muscle-computer interfaces. However, the existing approaches employed a very large deep convolutional neural network (ConvNet) architecture and complex training schemes for HD-sEMG image recognition, which requires learning of 5.63 million(M) training parameters only during fine-tuning and pre-trained on a very large-scale labeled HD-sEMG training d
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