HemCNN：深度学习能够在抓握运动任务中解码fNIRS皮层信号
HemCNN: Deep Learning enables decoding of fNIRS cortical signals in hand grip motor tasks
摘要：
我们使用数据驱动的方法，通过使用卷积神经网络架构HemCNN来解决fNIRS左/右手力解码问题。我们测试HemCNN的解码能力，以流式方式从fNIRS数据中解码左手或右手。HemCNN学会了以$~1\,$ Hz的自然手部动作速度检测哪只手执行抓握，优于标准方法。由于HemCNN不需要基线校正，并且卷积运算对时间平移是不变的，因此我们的方法可以帮助解锁各种实时任务的fNIRS。移动脑成像和移动脑机接口可以从中受益，以开发基于BOLD类信号的真实世界神经科学和实用人类神经接口，用于力生成的评估，辅助和康复，例如fNIRS与EEG信号的融合。
英文摘要：
We solve the fNIRS left/right hand force decoding problem using a data-driven approach by using a convolutional neural network architecture, the HemCNN. We test HemCNN's decoding capabilities to decode in a streaming way the hand, left or right, from fNIRS data. HemCNN learned to detect which hand executed a grasp at a naturalistic hand action speed of $~1\,$Hz, outperforming standard methods. Since HemCNN does not require baseline correction and the convolution operation is invariant to time translations, our method can help to unlock fNIRS for a variety of real-time tasks. Mobile brain imaging and mobile brain machine interfacing can benefit from this to develop real-world neuroscience and practical human neural interfacing based on BOLD-like signals for the evaluation, assistance and rehabilitation of force generation, such as fusion of fNIRS with EEG signals.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
