针对卷积神经网络(CNN)在感受野有限、缺乏对全局信息的有效感知,以及在处理短时稳态运动视觉诱发电位(SSMVEP)信号时分类效果欠佳的问题,提出了一种紧凑EEGNet-Transformer(即EEGNetformer)网络。EEGNetformer网络融合了为脑电(EEG)信...针对卷积神经网络(CNN)在感受野有限、缺乏对全局信息的有效感知,以及在处理短时稳态运动视觉诱发电位(SSMVEP)信号时分类效果欠佳的问题,提出了一种紧凑EEGNet-Transformer(即EEGNetformer)网络。EEGNetformer网络融合了为脑电(EEG)信号识别任务而设计的通用的卷积神经网络EEGNet网络和Transformer网络的优势,有效地捕捉与处理脑电信号中的局部和全局信息,增强网络对SSMVEP特征的学习,进而实现良好的解码性能。EEGNet网络用于提取SSMVEP的局部时间和空间特征,而Transformer网络用于捕捉脑电时间序列的全局信息。在基于SSMVEP-BCI范式采集的数据基础上,开展了实验以评估EEGNetformer网络的性能。实验结果显示,当在2 s SSMVEP数据条件下,EEGNetformer网络在基于被试者内情况的平均准确率为88.9%±6.6%,在基于跨被试者情况的平均准确率为69.1%±4.3%。与传统的CNN算法相比,EEGNetformer网络的分类性能提升了4.2%~17.4%。研究内容说明,EEGNetformer网络在有效提高SSMVEP-BCI识别准确率方面具有显著优势,为进一步提升SSMVEP-BCI解码性能提供了新的研究思路。展开更多
As a new type of brain-computer interface(BCI),the rapid serial visual presentation(RSVP)paradigm has attracted significant attention.The mechanism of RSVP is detecting the P300 component corresponding to the target i...As a new type of brain-computer interface(BCI),the rapid serial visual presentation(RSVP)paradigm has attracted significant attention.The mechanism of RSVP is detecting the P300 component corresponding to the target image to realize fast and correct recognition.This paper proposed an improved EEGNet model to achieve good performance in offline and online data.Specifically,the data were filtered by xDAWN to enhance the signal-to-noise ratio of the electroencephalogram(EEG)signals.The focal loss function was used instead of the cross-entropy loss function to solve the classification problems of unbalanced samples.Additionally,the subject-specific data were fed to the improved EEGNet model to obtain a subject-specific model.We applied the proposed model at the BCI Controlled Robot Contest in World Robot Contest 2021 and won the second place.The average recall rate of the four participants reached 51.56%in triple classification.In the offline data benchmark dataset(64 subjects-RSVP tasks),the average recall rates of groups A and B reached 76.07%and 78.11%,respectively.We provided an alternative method to identify targets based on the RSVP paradigm.展开更多
From August 19 to 21,2022,the BCI Controlled Robot Contest finals in the World Robot Contest 2022 were held in Beijing,China.Fifteen teams participated in the finals in the Algorithm Contest of Motor Imagery BCI.This ...From August 19 to 21,2022,the BCI Controlled Robot Contest finals in the World Robot Contest 2022 were held in Beijing,China.Fifteen teams participated in the finals in the Algorithm Contest of Motor Imagery BCI.This paper introduces the algorithms in the motor imagery(MI)classification area,describes the competition content and set,and summarizes the algorithms and results of the top five teams in the finals.First,the MI paradigm and the overview of the existing motor imagery brain–computer interface classification algorithms are introduced,followed by the introduction of the algorithms of the top five teams in the final step by step,including electroencephalography channel selection,data length selection,data preprocessing,data augmentation,classification network,training,and testing settings.Finally,the highlights and results of each algorithm are discussed.展开更多
Background:One of the most prestigious competitions in the world is the World Robot Conference.This paper presents the winning solution to the supervised motor imagery(MI)task in the BCI Controlled Robot Contest in Wo...Background:One of the most prestigious competitions in the world is the World Robot Conference.This paper presents the winning solution to the supervised motor imagery(MI)task in the BCI Controlled Robot Contest in World Robot Contest 2021.Methods:Data augmentation,preprocessing,feature extraction,and model training are the main components of the solution.The model is based on EEGNet,a popular convolutional neural networks model for classifying electroencephalography data.Results:Despite the model’s lack of stability,this solution was the most successful in the task.The channels closest to the vertex were the most helpful in feature extraction.Conclusion:This solution is suitable for supervised MI tasks not only in this competition but also in future scenarios.展开更多
文摘针对卷积神经网络(CNN)在感受野有限、缺乏对全局信息的有效感知,以及在处理短时稳态运动视觉诱发电位(SSMVEP)信号时分类效果欠佳的问题,提出了一种紧凑EEGNet-Transformer(即EEGNetformer)网络。EEGNetformer网络融合了为脑电(EEG)信号识别任务而设计的通用的卷积神经网络EEGNet网络和Transformer网络的优势,有效地捕捉与处理脑电信号中的局部和全局信息,增强网络对SSMVEP特征的学习,进而实现良好的解码性能。EEGNet网络用于提取SSMVEP的局部时间和空间特征,而Transformer网络用于捕捉脑电时间序列的全局信息。在基于SSMVEP-BCI范式采集的数据基础上,开展了实验以评估EEGNetformer网络的性能。实验结果显示,当在2 s SSMVEP数据条件下,EEGNetformer网络在基于被试者内情况的平均准确率为88.9%±6.6%,在基于跨被试者情况的平均准确率为69.1%±4.3%。与传统的CNN算法相比,EEGNetformer网络的分类性能提升了4.2%~17.4%。研究内容说明,EEGNetformer网络在有效提高SSMVEP-BCI识别准确率方面具有显著优势,为进一步提升SSMVEP-BCI解码性能提供了新的研究思路。
基金This work is granted by the Special Projects in Key Fields Supported by the Technology Development Project of Guangdong Province(Grant No.2020ZDZX3018)the Special Fund for Science and Technology of Guangdong Province(Grant No.2020182)+2 种基金the Wuyi University and Hong Kong&Macao Joint Research Project(Grant No.2019WGALH16)the Guangdong Basic and Applied Basic Research Foundation(Grant No.2020A1515111154)the Characteristic Innovation Projects of Ordinary Universities in Guangdong Province(Grant No.2021KTSCX136).
文摘As a new type of brain-computer interface(BCI),the rapid serial visual presentation(RSVP)paradigm has attracted significant attention.The mechanism of RSVP is detecting the P300 component corresponding to the target image to realize fast and correct recognition.This paper proposed an improved EEGNet model to achieve good performance in offline and online data.Specifically,the data were filtered by xDAWN to enhance the signal-to-noise ratio of the electroencephalogram(EEG)signals.The focal loss function was used instead of the cross-entropy loss function to solve the classification problems of unbalanced samples.Additionally,the subject-specific data were fed to the improved EEGNet model to obtain a subject-specific model.We applied the proposed model at the BCI Controlled Robot Contest in World Robot Contest 2021 and won the second place.The average recall rate of the four participants reached 51.56%in triple classification.In the offline data benchmark dataset(64 subjects-RSVP tasks),the average recall rates of groups A and B reached 76.07%and 78.11%,respectively.We provided an alternative method to identify targets based on the RSVP paradigm.
基金supported by the STI 2030—Major Project(Grant No.2021ZD0201300)Shenzhen Science and Technology Program(Grant No.JCYJ20220818103602004)Hubei Province Funds for Distinguished Young Scholars(Grant No.2020CFA050)。
文摘From August 19 to 21,2022,the BCI Controlled Robot Contest finals in the World Robot Contest 2022 were held in Beijing,China.Fifteen teams participated in the finals in the Algorithm Contest of Motor Imagery BCI.This paper introduces the algorithms in the motor imagery(MI)classification area,describes the competition content and set,and summarizes the algorithms and results of the top five teams in the finals.First,the MI paradigm and the overview of the existing motor imagery brain–computer interface classification algorithms are introduced,followed by the introduction of the algorithms of the top five teams in the final step by step,including electroencephalography channel selection,data length selection,data preprocessing,data augmentation,classification network,training,and testing settings.Finally,the highlights and results of each algorithm are discussed.
基金This work was granted by the Chinese National Programs for Brain Science and Brain-like Intelligence Technology(Grant No.2021ZD0202101)the National Natural Science Foundation of China(Grant Nos.32171080,32161143022,71942003,31800927,31900766 and 71874170)+2 种基金Major Project of Philosophy and Social Science Research from Ministry of Education of China(Grant No.19JZD010)CAS-VPST Silk Road Science Fund 2021(Grant No.GLHZ202128)Collaborative Innovation Program of Hefei Science Center from CAS(Grant No.2020HSC-CIP001).
文摘Background:One of the most prestigious competitions in the world is the World Robot Conference.This paper presents the winning solution to the supervised motor imagery(MI)task in the BCI Controlled Robot Contest in World Robot Contest 2021.Methods:Data augmentation,preprocessing,feature extraction,and model training are the main components of the solution.The model is based on EEGNet,a popular convolutional neural networks model for classifying electroencephalography data.Results:Despite the model’s lack of stability,this solution was the most successful in the task.The channels closest to the vertex were the most helpful in feature extraction.Conclusion:This solution is suitable for supervised MI tasks not only in this competition but also in future scenarios.
