Phase-amplitude coupling-based adaptive filters for neural signal decoding

Frontiers in Neuroscience

Neuroscience

Yu Qi¹  Jiajun Li²  Gang Pan² 

[1] State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China;Affiliated Mental Health Center and Hangzhou Seventh Peoples Hospital, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University School of Medicine, Hangzhou, China;State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China;College of Computer Science and Technology, Zhejiang University, Hangzhou, China;

关键词: electrocorticography (ECoG);    phase-amplitude coupling;    deep learning;    band-pass filter;    brain-computer interface;   

DOI  :  10.3389/fnins.2023.1153568

 received in 2023-01-29, accepted in 2023-04-06,  发布年份 2023

来源: Frontiers

【 摘 要 】

Bandpass filters play a core role in ECoG signal processing. Commonly used frequency bands such as alpha, beta, and gamma bands can reflect the normal rhythm of the brain. However, the universally predefined bands might not be optimal for a specific task. Especially the gamma band usually covers a wide frequency span (i.e., 30–200 Hz) which can be too coarse to capture features that appear in narrow bands. An ideal option is to find the optimal frequency bands for specific tasks in real-time and dynamically. To tackle this problem, we propose an adaptive band filter that selects the useful frequency band in a data-driven way. Specifically, we leverage the phase-amplitude coupling (PAC) of the coupled working mechanism of synchronizing neuron and pyramidal neurons in neuronal oscillations, in which the phase of slower oscillations modulates the amplitude of faster ones, to help locate the fine frequency bands from the gamma range, in a task-specific and individual-specific way. Thus, the information can be more precisely extracted from ECoG signals to improve neural decoding performance. Based on this, an end-to-end decoder (PACNet) is proposed to construct a neural decoding application with adaptive filter banks in a uniform framework. Experiments show that PACNet can improve neural decoding performance universally with different tasks.

【 授权许可】

Unknown   
Copyright © 2023 Li, Qi and Pan.