Crossing time windows optimization based on mutual information for hybrid BCI

Hybrid EEG-fNIRS brain-computer interface (HBCI) is widely employed to enhance BCI performance. EEG and fNIRS signals are combined to increase the dimensionality of the information. Time windows are used to select EEG and fNIRS singles synchronously. However, it ignores that specific modal signals h...

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Autores principales:	Ming Meng, Luyang Dai, Qingshan She, Yuliang Ma, Wanzeng Kong
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	eeg fnirs mental arithmetic crossing time window sparse Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/91325d3a99a3455987e0bbcdf13add67
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id	oai:doaj.org-article:91325d3a99a3455987e0bbcdf13add67
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spelling	oai:doaj.org-article:91325d3a99a3455987e0bbcdf13add672021-11-23T02:57:07ZCrossing time windows optimization based on mutual information for hybrid BCI10.3934/mbe.20213921551-0018https://doaj.org/article/91325d3a99a3455987e0bbcdf13add672021-09-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021392?viewType=HTMLhttps://doaj.org/toc/1551-0018Hybrid EEG-fNIRS brain-computer interface (HBCI) is widely employed to enhance BCI performance. EEG and fNIRS signals are combined to increase the dimensionality of the information. Time windows are used to select EEG and fNIRS singles synchronously. However, it ignores that specific modal signals have their own characteristics, when the task is stimulated, the information between the modalities will mismatch at the moment, which has a significant impact on the classification performance. Here we propose a novel crossing time windows optimization for mental arithmetic (MA) based BCI. The EEG and fNIRS signals were segmented separately by sliding time windows. Then crossing time windows (CTW) were combined with each one segment from EEG and fNIRS selected independently. Furthermore, EEG and fNIRS features were extracted using Filter Bank Common Spatial Pattern (FBCSP) and statistical methods from each sample. Mutual information was calculated for FBCSP and statistical features to characterize the discrimination of crossing time windows, and the optimal window would be selected based on the largest mutual information. Finally, a sparse structured framework of Fisher Lasso feature selection (FLFS) was designed to select the joint features, and conventional Linear Discriminant Analysis (LDA) was employed to perform classification. We used proposed method for a MA dataset. The classification accuracy of the proposed method is 92.52 ± 5.38% and higher than other methods, which shows the rationality and superiority of the proposed method.Ming Meng Luyang DaiQingshan SheYuliang MaWanzeng KongAIMS Pressarticleeegfnirsmental arithmeticcrossing time windowsparseBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 7919-7935 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	eeg fnirs mental arithmetic crossing time window sparse Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	eeg fnirs mental arithmetic crossing time window sparse Biotechnology TP248.13-248.65 Mathematics QA1-939 Ming Meng Luyang Dai Qingshan She Yuliang Ma Wanzeng Kong Crossing time windows optimization based on mutual information for hybrid BCI
description	Hybrid EEG-fNIRS brain-computer interface (HBCI) is widely employed to enhance BCI performance. EEG and fNIRS signals are combined to increase the dimensionality of the information. Time windows are used to select EEG and fNIRS singles synchronously. However, it ignores that specific modal signals have their own characteristics, when the task is stimulated, the information between the modalities will mismatch at the moment, which has a significant impact on the classification performance. Here we propose a novel crossing time windows optimization for mental arithmetic (MA) based BCI. The EEG and fNIRS signals were segmented separately by sliding time windows. Then crossing time windows (CTW) were combined with each one segment from EEG and fNIRS selected independently. Furthermore, EEG and fNIRS features were extracted using Filter Bank Common Spatial Pattern (FBCSP) and statistical methods from each sample. Mutual information was calculated for FBCSP and statistical features to characterize the discrimination of crossing time windows, and the optimal window would be selected based on the largest mutual information. Finally, a sparse structured framework of Fisher Lasso feature selection (FLFS) was designed to select the joint features, and conventional Linear Discriminant Analysis (LDA) was employed to perform classification. We used proposed method for a MA dataset. The classification accuracy of the proposed method is 92.52 ± 5.38% and higher than other methods, which shows the rationality and superiority of the proposed method.
format	article
author	Ming Meng Luyang Dai Qingshan She Yuliang Ma Wanzeng Kong
author_facet	Ming Meng Luyang Dai Qingshan She Yuliang Ma Wanzeng Kong
author_sort	Ming Meng
title	Crossing time windows optimization based on mutual information for hybrid BCI
title_short	Crossing time windows optimization based on mutual information for hybrid BCI
title_full	Crossing time windows optimization based on mutual information for hybrid BCI
title_fullStr	Crossing time windows optimization based on mutual information for hybrid BCI
title_full_unstemmed	Crossing time windows optimization based on mutual information for hybrid BCI
title_sort	crossing time windows optimization based on mutual information for hybrid bci
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/91325d3a99a3455987e0bbcdf13add67
work_keys_str_mv	AT mingmeng crossingtimewindowsoptimizationbasedonmutualinformationforhybridbci AT luyangdai crossingtimewindowsoptimizationbasedonmutualinformationforhybridbci AT qingshanshe crossingtimewindowsoptimizationbasedonmutualinformationforhybridbci AT yuliangma crossingtimewindowsoptimizationbasedonmutualinformationforhybridbci AT wanzengkong crossingtimewindowsoptimizationbasedonmutualinformationforhybridbci
_version_	1718417388326617088

Crossing time windows optimization based on mutual information for hybrid BCI

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