Protocol for assisting frequency band definition and decoding neural dynamics using hierarchical clustering and multivariate pattern analysis

• Published in: STAR protocols Vol. 6; no. 2; p. 103870
• Main Authors: Li, Chengpeng, Hasegawa, Isao, Tanigawa, Hisashi
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 20.06.2025; Elsevier
• Subjects: Animals; Biotechnology and bioengineering; Brain - physiology; Cluster Analysis; Cognitive Neuroscience; Electrocorticography - methods; Macaca; Multivariate Analysis; Neuroscience; Protocol; Signal Processing, Computer-Assisted; Biotechnology and bioengineering; Cognitive Neuroscience; Neuroscience
• ISSN: 2666-1667; 2666-1667
• DOI: 10.1016/j.xpro.2025.103870

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.1 [Display omitted] •Steps for defining frequency bands via hierarchical clustering•Procedures for time-frequency analysis and data clustering•Instructions for MVPA validation of derived frequency bands•Guidance on identifying functionally distinct neural sub-bands Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. 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.