基于图表征和双重注意力机制的跨被试ERP检测

TP391; 针对事件相关电位(event-related potential,ERP)在跨被试场景下检测精度不高的问题,提出了一 种基于图表征和双重注意力机制的卷积循环神经网络模型.该模型采用不依赖于被试和任务的图来表征脑电信号中的空间信息,并级联卷积神经网络(convolutional neural network,CNN)和长短期记忆网络(long short-term memory net-work,LSTM)形成CNN-LSTM基础框架,同时嵌入双重注意力机制(即选择性内核卷积和自注意力机制)以充分提取不同被试脑电信号的时空特征,从而提高跨被试场景下的ERP检测精度.在基于快速序列视...

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Published in计算机工程与应用 Vol. 59; no. 11; pp. 160 - 167
Main Authors 相晓嘉, 兰珍, 闫超, 李子杏, 唐邓清, 周晗
Format Journal Article
LanguageChinese
Published 国防科技大学智能科学学院,长沙 410073 01.06.2023
Subjects
图表征
注意力机制
卷积循环模型
脑电图(EEG)
相关电位(ERP)检测
Online AccessGet full text
ISSN1002-8331
DOI10.3778/j.issn.1002-8331.2202-0311

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Abstract TP391; 针对事件相关电位(event-related potential,ERP)在跨被试场景下检测精度不高的问题,提出了一 种基于图表征和双重注意力机制的卷积循环神经网络模型.该模型采用不依赖于被试和任务的图来表征脑电信号中的空间信息,并级联卷积神经网络(convolutional neural network,CNN)和长短期记忆网络(long short-term memory net-work,LSTM)形成CNN-LSTM基础框架,同时嵌入双重注意力机制(即选择性内核卷积和自注意力机制)以充分提取不同被试脑电信号的时空特征,从而提高跨被试场景下的ERP检测精度.在基于快速序列视觉呈现范式的大规模基准数据集上的实验结果表明,与现有的7种ERP检测方法相比,所提方法在跨被试场景下具有显著的优越性.
AbstractList TP391; 针对事件相关电位(event-related potential,ERP)在跨被试场景下检测精度不高的问题,提出了一 种基于图表征和双重注意力机制的卷积循环神经网络模型.该模型采用不依赖于被试和任务的图来表征脑电信号中的空间信息,并级联卷积神经网络(convolutional neural network,CNN)和长短期记忆网络(long short-term memory net-work,LSTM)形成CNN-LSTM基础框架,同时嵌入双重注意力机制(即选择性内核卷积和自注意力机制)以充分提取不同被试脑电信号的时空特征,从而提高跨被试场景下的ERP检测精度.在基于快速序列视觉呈现范式的大规模基准数据集上的实验结果表明,与现有的7种ERP检测方法相比,所提方法在跨被试场景下具有显著的优越性.
Author 闫超
李子杏
唐邓清
相晓嘉
周晗
兰珍
AuthorAffiliation 国防科技大学智能科学学院,长沙 410073
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Author_FL LAN Zhen
TANG Dengqing
XIANG Xiaojia
LI Zixing
YAN Chao
ZHOU Han
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Issue 11
Keywords 图表征
注意力机制
卷积循环模型
脑电图(EEG)
相关电位(ERP)检测
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Snippet TP391; 针对事件相关电位(event-related potential,ERP)在跨被试场景下检测精度不高的问题,提出了一 种基于图表征和双重注意力机制的卷积循环神经网络模型.该模型采用不依赖于被试和任务的图来表征脑电信号中的空间信息,并级联卷积神经网络(convolutional neural...
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StartPage 160
Title 基于图表征和双重注意力机制的跨被试ERP检测
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