基于P300特征的脑-机接口解码算法研究综述

A Review of Brain-computer Interface Decoding Algorithms Based on P300 Features

脑-机接口是脑与外部设备连接进而实现信息交换技术,核心是发挥人脑的优势,直接通过人脑与外部环境进行高效互动,目前已成为世界各国的研究热点。P300是由特定刺激范式激发的事件相关电位中的一个典型成分,最初研究发现,当人脑受到某些小概率事件刺激时,会在脑电信号中出现一个潜伏期约为300 ms的正向波峰。由于P300成分具有刺激特异性,因此该特性已被广泛用来研究注意、辨认等认知功能,基于P300特征的脑电范式也已经成为三大主流范式之一。近年来,结合P300和脑-机接口的具有实用功能的应用大量涌现,与此相关的解码算法也被提出并不断改进,这为人们研究P300脑-机接口提供了便利。然而,P300脑-机接口的相关解码算法缺乏系统性总结,各类算法之间的特点也尚未得到明确比较。该文依据P300成分主要在顶枕区响应的特点,提出了基于非空间信息的P300脑电解码算法;依据P300成分在不同脑区响应特征不同的特点,提出了基于空间信息的P300脑电解码算法;依据近几年信息技术快速发展,深度学习已不断在P300脑-机接口中应用的特点,提出了基于深度学习的新型P300脑电解码算法;从以上三个角度入手,总结近年来P300脑-机接口解码算法的研究进展,并根据现有的P300公开数据集对算法性能进行比较,阐明各算法优缺点及应用范围,最后再进一步探讨现存的典型问题以及未来可能的研究方向。

Brain-computer interface(BCI)is a technology that connects the brain with external devices to achieve information exchange.The core is to bring the advantages of the human brain into play and interact with the external environment directly through the human brain.It has become a research hotspot all over the world.P300 is a typical component of event-related potentials triggered by a specific stimulus paradigm.Initial studies found that when the human brain is stimulated by certain small probability events,a positive peak with a latency of about 300 ms appears in the EEG signal.Because the P300 component has stimulation specificity,it has been widely used to study cognitive functions such as attention and recognition.The EEG paradigm based on the P300 feature has also become one of the three mainstream paradigms.In recent years,there have been a large number of applications with practical functions combining P300 and braincomputer interface,and related decoding algorithms have been proposed and continuously improved,which makes it convenient for people to study P300 brain-computer interface.However,the related decoding algorithms of the P300 braincomputer interface are not systematically summarized,and the characteristics of the algorithms have not been clearly compared.In this paper,a P300 EEG decoding algorithm based on non-spatial information is proposed according to the feature that P300 components mainly respond in the parietal-occipital region.Based on the different response characteristics of P300 components in different brain regions,a P300 EEG decoding algorithm based on spatial information is proposed.Based on the rapid development of information technology in recent years and the characteristics of deep learning in the application of P300 brain-computer interface,a new P300 EEG decoding algorithm based on deep learning is proposed.Starting from the above three perspectives,this paper summarizes the research progress of P300 brain-computer interface decoding algorithms in recent years and the characteristics of deep learning in the application of P300 brain-computer interface,a new P300 EEG decoding algorithm based on deep learning is proposed.Starting from the above three perspectives,summarize the research progress of P300 brain-computer interface decoding algorithms in recent years,and compare the performance of algorithms based on existing P300 public datasets,clarify the advantages,disadvantages,and application scope of each algorithm.Finally,further explore the existing typical problems and potential research directions in the future.