{"title":"基于深度森林算法的多通道近红外系统前额叶心理任务分类","authors":"Yizhen Wen, Xiangao Qi, Shaoyang Cui, Cheng Chen, Mingye Chen, Jian Zhao, Guoxing Wang","doi":"10.1109/BIOCAS.2019.8919082","DOIUrl":null,"url":null,"abstract":"This paper presents a multi-channel continuous-wave near-infrared spectroscopy (NIRS) system, which is applied to classify different cortical activation states of the prefrontal cortex (PFC). Mental arithmetic, digit span, semantic task and relax were selected as four mental tasks. A deep forest algorithm is employed to achieve high classification accuracy. With employing multi-grained scanning to NIRS data, this system can extract the structural features and result in higher performance. The proposed system with proper optimization can achieve 85.7% accuracy on the self-built dataset, which is the highest results compared to the existing systems.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"A Multi-channel NIRS System for Prefrontal Mental Task Classification Employing Deep Forest Algorithm\",\"authors\":\"Yizhen Wen, Xiangao Qi, Shaoyang Cui, Cheng Chen, Mingye Chen, Jian Zhao, Guoxing Wang\",\"doi\":\"10.1109/BIOCAS.2019.8919082\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a multi-channel continuous-wave near-infrared spectroscopy (NIRS) system, which is applied to classify different cortical activation states of the prefrontal cortex (PFC). Mental arithmetic, digit span, semantic task and relax were selected as four mental tasks. A deep forest algorithm is employed to achieve high classification accuracy. With employing multi-grained scanning to NIRS data, this system can extract the structural features and result in higher performance. The proposed system with proper optimization can achieve 85.7% accuracy on the self-built dataset, which is the highest results compared to the existing systems.\",\"PeriodicalId\":222264,\"journal\":{\"name\":\"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIOCAS.2019.8919082\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIOCAS.2019.8919082","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Multi-channel NIRS System for Prefrontal Mental Task Classification Employing Deep Forest Algorithm
This paper presents a multi-channel continuous-wave near-infrared spectroscopy (NIRS) system, which is applied to classify different cortical activation states of the prefrontal cortex (PFC). Mental arithmetic, digit span, semantic task and relax were selected as four mental tasks. A deep forest algorithm is employed to achieve high classification accuracy. With employing multi-grained scanning to NIRS data, this system can extract the structural features and result in higher performance. The proposed system with proper optimization can achieve 85.7% accuracy on the self-built dataset, which is the highest results compared to the existing systems.
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