Pouya Khani, Vahid Solouk, Hashem Kalbkhani, Farid Ahmadi
{"title":"融合 fNIRS 信号的 2D-DOST 深度特征,实现与受试者无关的运动执行任务分类","authors":"Pouya Khani, Vahid Solouk, Hashem Kalbkhani, Farid Ahmadi","doi":"10.1155/2023/3178284","DOIUrl":null,"url":null,"abstract":"Functional near-infrared spectroscopy (fNIRS) is a low-cost and noninvasive method to measure the hemodynamic responses of cortical brain activities and has received great attention in brain-computer interface (BCI) applications. In this paper, we present a method based on deep learning and the time-frequency map (TFM) of fNIRS signals to classify the three motor execution tasks including right-hand tapping, left-hand tapping, and foot tapping. To simultaneously obtain the TFM and consider the correlation among channels, we propose to utilize the two-dimensional discrete orthonormal Stockwell transform (2D-DOST). The TFMs for oxygenated hemoglobin (HbO), reduced hemoglobin (HbR), and two linear combinations of them are obtained and then we propose three fusion schemes for combining their deep information extracted by the convolutional neural network (CNN). Two CNNs, LeNet and MobileNet, are considered and their structures are modified to maximize the accuracy. Due to the lack of enough signals for training CNNs, data augmentation based on the Wasserstein generative adversarial network (WGAN) is performed. Several simulations are performed to assess the performance of the proposed method in three-class and binary scenarios. The results present the efficiency of the proposed method in different scenarios. Also, the proposed method outperforms the recently introduced methods.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":5.0000,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fusion of Deep Features from 2D-DOST of fNIRS Signals for Subject-Independent Classification of Motor Execution Tasks\",\"authors\":\"Pouya Khani, Vahid Solouk, Hashem Kalbkhani, Farid Ahmadi\",\"doi\":\"10.1155/2023/3178284\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Functional near-infrared spectroscopy (fNIRS) is a low-cost and noninvasive method to measure the hemodynamic responses of cortical brain activities and has received great attention in brain-computer interface (BCI) applications. In this paper, we present a method based on deep learning and the time-frequency map (TFM) of fNIRS signals to classify the three motor execution tasks including right-hand tapping, left-hand tapping, and foot tapping. To simultaneously obtain the TFM and consider the correlation among channels, we propose to utilize the two-dimensional discrete orthonormal Stockwell transform (2D-DOST). The TFMs for oxygenated hemoglobin (HbO), reduced hemoglobin (HbR), and two linear combinations of them are obtained and then we propose three fusion schemes for combining their deep information extracted by the convolutional neural network (CNN). Two CNNs, LeNet and MobileNet, are considered and their structures are modified to maximize the accuracy. Due to the lack of enough signals for training CNNs, data augmentation based on the Wasserstein generative adversarial network (WGAN) is performed. Several simulations are performed to assess the performance of the proposed method in three-class and binary scenarios. The results present the efficiency of the proposed method in different scenarios. Also, the proposed method outperforms the recently introduced methods.\",\"PeriodicalId\":14089,\"journal\":{\"name\":\"International Journal of Intelligent Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.0000,\"publicationDate\":\"2023-12-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Intelligent Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1155/2023/3178284\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Intelligent Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1155/2023/3178284","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Fusion of Deep Features from 2D-DOST of fNIRS Signals for Subject-Independent Classification of Motor Execution Tasks
Functional near-infrared spectroscopy (fNIRS) is a low-cost and noninvasive method to measure the hemodynamic responses of cortical brain activities and has received great attention in brain-computer interface (BCI) applications. In this paper, we present a method based on deep learning and the time-frequency map (TFM) of fNIRS signals to classify the three motor execution tasks including right-hand tapping, left-hand tapping, and foot tapping. To simultaneously obtain the TFM and consider the correlation among channels, we propose to utilize the two-dimensional discrete orthonormal Stockwell transform (2D-DOST). The TFMs for oxygenated hemoglobin (HbO), reduced hemoglobin (HbR), and two linear combinations of them are obtained and then we propose three fusion schemes for combining their deep information extracted by the convolutional neural network (CNN). Two CNNs, LeNet and MobileNet, are considered and their structures are modified to maximize the accuracy. Due to the lack of enough signals for training CNNs, data augmentation based on the Wasserstein generative adversarial network (WGAN) is performed. Several simulations are performed to assess the performance of the proposed method in three-class and binary scenarios. The results present the efficiency of the proposed method in different scenarios. Also, the proposed method outperforms the recently introduced methods.
期刊介绍:
The International Journal of Intelligent Systems serves as a forum for individuals interested in tapping into the vast theories based on intelligent systems construction. With its peer-reviewed format, the journal explores several fascinating editorials written by today''s experts in the field. Because new developments are being introduced each day, there''s much to be learned — examination, analysis creation, information retrieval, man–computer interactions, and more. The International Journal of Intelligent Systems uses charts and illustrations to demonstrate these ground-breaking issues, and encourages readers to share their thoughts and experiences.