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Titlebook: Brain-Computer Interfaces; Current Trends and A Aboul Ella Hassanien,Ahmad Taher Azar Book 2015 Springer International Publishing Switzerla

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發(fā)表于 2025-3-21 19:06:39 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱(chēng)Brain-Computer Interfaces
期刊簡(jiǎn)稱(chēng)Current Trends and A
影響因子2023Aboul Ella Hassanien,Ahmad Taher Azar
視頻videohttp://file.papertrans.cn/191/190296/190296.mp4
發(fā)行地址Presents current trends and applications in Brain Computer Interface technology.Presents novel paradigms for EEG signal recording, advanced methods for processing them, new applications for Brain Comp
學(xué)科分類(lèi)Intelligent Systems Reference Library
圖書(shū)封面Titlebook: Brain-Computer Interfaces; Current Trends and A Aboul Ella Hassanien,Ahmad Taher Azar Book 2015 Springer International Publishing Switzerla
影響因子.The success of a BCI system depends as much on the system itself as on the user’s ability to produce distinctive EEG activity. BCI systems can be divided into two groups according to the placement of the electrodes used to detect and measure neurons firing in the brain. These groups are: invasive systems, electrodes are inserted directly into the cortex are used for single cell or multi unit recording, and electrocorticography (EcoG), electrodes are placed on the surface of the cortex (or dura); noninvasive systems, they are placed on the scalp and use electroencephalography (EEG) or magnetoencephalography (MEG) to detect neuron activity..The book is basically divided into three parts. The first part of the book covers the basic concepts and overviews of Brain Computer Interface. The second part describes new theoretical developments of BCI systems. The third part covers views on real applications of BCI systems..
Pindex Book 2015
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書(shū)目名稱(chēng)Brain-Computer Interfaces影響因子(影響力)




書(shū)目名稱(chēng)Brain-Computer Interfaces影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Brain-Computer Interfaces網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Brain-Computer Interfaces網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Brain-Computer Interfaces被引頻次




書(shū)目名稱(chēng)Brain-Computer Interfaces被引頻次學(xué)科排名




書(shū)目名稱(chēng)Brain-Computer Interfaces年度引用




書(shū)目名稱(chēng)Brain-Computer Interfaces年度引用學(xué)科排名




書(shū)目名稱(chēng)Brain-Computer Interfaces讀者反饋




書(shū)目名稱(chēng)Brain-Computer Interfaces讀者反饋學(xué)科排名




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Source Localization for Brain-Computer Interfacesthe EEG signal by its spatial features. This chapter is dedicated to the essential theory related to electromagnetic source localization problem with a particular focus on the family of sparse localization approaches. First we discuss general electromagnetic head modelling methods used to solve the
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Hippocampal Theta-Based Brain Computer Interface and substantial—theta-contingent training produces a two- to four-fold increase in learning speed, accompanied by striking differences in hippocampal, prefrontal and cerebellar electrophysiological patterns. Unlike many interfaces that serve as sensory or motor prostheses, our system appears to eng
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Detection of Human Emotions Using Features Based on the Multiwavelet Transform of EEG Signalset decomposition of EEG signals. These features have been used as an input to multiclass least squares support vector machine (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for classification of human emotions from EEG signals. The
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Mood Recognition System Using EEG Signal of Song Induced Activitiesucted for 25?min, with eye closed and each subject was asked to concentrate on the given tasks. In this study, we have created EEG dataset containing data of five mental tasks of ten different subjects. We determine the alpha rhythms in the left hemisphere are more predominant over the right hemisph
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