Hemodynamic Response Detection Using Integrated EEG-fNIRS-VPA for BCI

作     者：Arshia Arif M.Jawad Khan Kashif Javed Hasan Sajid Saddaf Rubab Noman Naseer Talha Irfan Khan 

作者机构：National University of Sciences and Technology(NUST)IslamabadPakistan Intelligent Robotics LabNational Center of Artificial IntelligenceNational University of Sciences and Technology(NUST)IslamabadPakistan Department of Mechatronics EngineeringAir UniversityIslamabadPakistan Institute of Space TechnologyIslamabadPakistan 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第70卷第1期

页      面：535-555页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 1002[医学-临床医学] 0809[工学-电子科学与技术（可授工学、理学学位）] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：National University of Sciences and Technology supported the research 

主　　题：EEG-fNIRS hybrid BCI vector-phase analysis hemodynamic response detection 

摘      要：For BCI systems,it is important to have an accurate and less complex architecture to control a device with enhanced *** this paper,a novel methodology for more accurate detection of the hemodynamic response has been developed using a multimodal brain-computer interface(BCI).An integrated classifier has been developed for achieving better classification accuracy using two *** integrated EEG-fNIRS-based vector-phase analysis(VPA)has been *** open-source dataset collected at the TechnischeUniversit鋞Berlin,including simultaneous electroencephalography(EEG)and functional near-infrared spectroscopy(fNIRS)signals of 26 healthy participants during n-back tests,has been used for this *** and physiological noise removal has been done using preprocessing techniques followed by individually detecting activity in both *** resting state threshold circle,VPA has been used to detect a hemodynamic response in fNIRS signals,whereas phase plots for EEG signals have been constructed using Hilbert Transform to detect the activity in each *** threshold circles are drawn in the vector plane,where each circle is drawn after task completion in each trial of EEG ***,both processes are integrated into one vector-phase plot to get combined detection of hemodynamic response for *** of this study illustrate that the combined EEG-fNIRS VPA yields considerably higher average classification accuracy,that is 91.35%,as compared to other classifiers such as support vector machine(SVM),convolutional neural networks(CNN),deep neural networks(DNN)and VPA(with dual-threshold circles)with classification accuracies 82%,89%,87%and 86%*** of this research demonstrate that improved classification performance can be feasibly achieved using multimodal VPA for EEG-fNIRS hybrid data.