Assessing Neuromorphic Computing for Fingertip Force Decoding from Electromyography
评估神经形态计算在从肌电信号解码指尖力中的应用
机构 * Department of Electrical and Computer Engineering, NC State University, USA（电气与计算机工程系，北卡罗来纳州立大学） ； Lampe Joint Department of Biomedical Engineering, NC State University / University of North Carolina at Chapel Hill, USA（拉姆佩联合生物医学工程系，北卡罗来纳州立大学 / 北卡罗来纳大学教堂山分校）
专题命中 语音与意图解码 ：neural interface(abstract)；分类 eess.SP、cs.LG
AI总结 本文评估了脉冲神经网络在从肌电信号解码指尖力中的应用，发现TCN在精度上优于SNN，但SNN作为神经形态架构具有潜在的改进空间。
Comments 5 pages, 6 figures. Poster included as ancillary file (IEEE_NER2025_NeuromorphicEMG_poster.pdf). Presented at IEEE EMBS NER 2025, also at NC State College of Engineering Applied AI Symposium and NC State ECE Graduate Research Symposium (tied for Best Poster)
