Evaluation of LDA Ensembles Classifiers for Brain Computer Interface
نویسندگان
چکیده
منابع مشابه
An Enhanced Probabilistic LDA for Multi-Class Brain Computer Interface
BACKGROUND There is a growing interest in the study of signal processing and machine learning methods, which may make the brain computer interface (BCI) a new communication channel. A variety of classification methods have been utilized to convert the brain information into control commands. However, most of the methods only produce uncalibrated values and uncertain results. METHODOLOGY/PRINC...
متن کامل0 Applied Advanced Classifiers for Brain Computer Interface
Since that Dr. Hans Berger discovered the electrical nature of the brain, it has been considered the possibility to communicate personswith external devices only through the use of the brain waves (Vidal, 1973). Brain Computer Interface technology is aimed at communicating with persons using external computerised devices via the electroencephalographic signal as the primary command source (Wolp...
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Brain-computer interfaces (BCI) record brain signals, analyze and translate them into control commands which are relayed to output devices that carry out desired actions. These systems do not use normal neuromuscular output pathways. Actually, the principal goal of BCI systems is to provide better life style for physically-challenged people which are suffered from cerebral palsy, amyotrophic l...
متن کاملEvaluation of Classical Features and Classifiers in Brain-Computer Interface Tasks
Brain-Computer Interface (BCI) uses brain signals in order to provide a new method for communication between human and outside world. Feature extraction, selection and classification are among the main matters of concerns in signal processing stage of BCI. In this article, we present our findings about the most effective features and classifiers in some brain tasks. Six different groups of clas...
متن کاملEnhanced Z-LDA for Small Sample Size Training in Brain-Computer Interface Systems
BACKGROUND Usually the training set of online brain-computer interface (BCI) experiment is small. For the small training set, it lacks enough information to deeply train the classifier, resulting in the poor classification performance during online testing. METHODS In this paper, on the basis of Z-LDA, we further calculate the classification probability of Z-LDA and then use it to select the ...
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ژورنال
عنوان ژورنال: Journal of Physics: Conference Series
سال: 2011
ISSN: 1742-6596
DOI: 10.1088/1742-6596/332/1/012025