完整後設資料紀錄
DC 欄位語言
dc.contributor.authorWu, Shang-Linen_US
dc.contributor.authorLiu, Yu-Tingen_US
dc.contributor.authorChou, Kuang-Penen_US
dc.contributor.authorLin, Yang-Yinen_US
dc.contributor.authorLu, Jieen_US
dc.contributor.authorZhang, Guangquanen_US
dc.contributor.authorChuang, Chun-Hsiangen_US
dc.contributor.authorLin, Wen-Chiehen_US
dc.contributor.authorLin, Chin-Tengen_US
dc.date.accessioned2017-04-21T06:49:21Z-
dc.date.available2017-04-21T06:49:21Z-
dc.date.issued2016en_US
dc.identifier.isbn978-1-5090-0625-0en_US
dc.identifier.issn1544-5615en_US
dc.identifier.urihttp://hdl.handle.net/11536/134567-
dc.description.abstractA brain-computer interface (BCI) system provides a convenient means of communication between the human brain and a computer, which is applied not only to healthy people but also for people that suffer from motor neuron diseases (MNDs). Motor imagery (MI) is one well-known basis for designing Electroencephalography (EEG)-based real-life BCI systems. However, EEG signals are often contaminated with severe noise and various uncertainties, imprecise and incomplete information streams. Therefore, this study proposes spectrum ensemble based on swam-optimized fuzzy integral for integrating decisions from sub-band classifiers that are established by a sub-band common spatial pattern (SBCSP) method. Firstly, the SBCSP effectively extracts features from EEG signals, and thereby the multiple linear discriminant analysis (MLDA) is employed during a MI classification task. Subsequently, particle swarm optimization (PSO) is used to regulate the subject-specific parameters for assigning optimal confidence levels for classifiers used in the fuzzy integral during the fuzzy fusion stage of the proposed system. Moreover, BCI systems usually tend to have complex architectures, be bulky in size, and require time-consuming processing. To overcome this drawback, a wireless and wearable EEG measurement system is investigated in this study. Finally, in our experimental result, the proposed system is found to produce significant improvement in terms of the receiver operating characteristic (ROC) curve. Furthermore, we demonstrate that a robotic arm can be reliably controlled using the proposed BCI system. This paper presents novel insights regarding the possibility of using the proposed MI-based BCI system in real-life applications.en_US
dc.language.isoen_USen_US
dc.subjectBrain-computer interface (BCI)en_US
dc.subjectMotor imagery (MI)en_US
dc.subjectElectroencephalography (EEG)en_US
dc.subjectFuzzy integralen_US
dc.subjectParticle swarm optimization (PSO)en_US
dc.titleA Motor Imagery Based Brain-Computer Interface System via Swarm-Optimized Fuzzy Integral and Its Applicationen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2016 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)en_US
dc.citation.spage2495en_US
dc.citation.epage2500en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.department電控工程研究所zh_TW
dc.contributor.department腦科學研究中心zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.contributor.departmentBrain Research Centeren_US
dc.identifier.wosnumberWOS:000392150700348en_US
dc.citation.woscount0en_US
顯示於類別:會議論文