使用层次聚类和多变量模式分析辅助频段定义和解码神经动力学的方案。
Protocol for assisting frequency band definition and decoding neural dynamics using hierarchical clustering and multivariate pattern analysis.
作者信息
Li Chengpeng, Hasegawa Isao, Tanigawa Hisashi
机构信息
Interdisciplinary Institute of Neuroscience and Technology, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, China; College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China; MOE Frontier Science Center for Brain Science and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, China; National Key Laboratory of Brain and Computer Intelligence, Zhejiang University, Hangzhou, China.
Department of Physiology, Niigata University School of Medical and Dental Sciences, Niigata, Japan.
出版信息
STAR Protoc. 2025 Jun 20;6(2):103870. doi: 10.1016/j.xpro.2025.103870. Epub 2025 Jun 3.
Traditional fixed frequency band divisions often limit neural data analysis accuracy. Here, we present a protocol for assisting frequency band definition for multichannel neural data using macaque electrocorticography (ECoG) data. We describe steps for performing time-frequency analysis on preprocessed signals and applying hierarchical clustering to frequency power profiles to identify data-informed groupings. We then detail procedures for defining frequency bands guided by these clusters and using multivariate pattern analysis (MVPA) on the derived bands for functional validation via time-series decoding. For complete details on the use and execution of this protocol, please refer to Tanigawa et al..
传统的固定频段划分常常限制神经数据分析的准确性。在此，我们提出一种协议，用于使用猕猴皮层脑电图（ECoG）数据辅助多通道神经数据的频段定义。我们描述了对预处理信号进行时频分析以及对频率功率分布应用层次聚类以识别基于数据的分组的步骤。然后，我们详细说明了由这些聚类引导定义频段以及对导出频段使用多变量模式分析（MVPA）通过时间序列解码进行功能验证的程序。有关此协议使用和执行的完整详细信息，请参考谷川等人的研究。
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