Analysis of Feature Extraction Algorithms Used in Brain-Computer Interfaces
نویسندگان
چکیده
منابع مشابه
Comparison of Feature Extraction Methods for Brain-Computer Interfaces
This paper compares classification accuracies of feature extraction methods (FEMs) as used in sensory motor rhythm (SMR) based Brain-Computer Interfaces (BCIs). Features were extracted offline from 9 subjects and classified with linear discriminant analysis. The following FEMs were compared: adaptive autoregressive parameters, band power, phase locking value, time domain parameters, and Hjorth ...
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Brain-Computer Interface (BCI) systems are a means of establishing communication for severely paralyzed patients. Based on the brain activity signals during the execution of mental tasks by a user, a computer system translates those signals first into higher-level features and finally into control commands for communication interfaces. This involves a number of algorithmic steps that have to be...
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Brain-Computer Interfaces (BCIs) are communication systems, which enable users to send commands to computers by using brain activity only; this activity being generally measured by Electroencephalography (EEG). BCIs are generally designed according to a pattern recognition approach, i.e., by extracting features from EEG signals, and by using a classifier to identify the user’s mental state from...
متن کاملEEG Based Brain Computer Interface Hand Grasp Control: Feature Extraction Method MTCSP
Brain-Computer Interfaces (BCIs) are communication systems, which enable users to send commands to computers by using brain activity only; this activity being generally measured by Electroencephalography (EEG). BCIs are generally designed according to a pattern recognition approach, i.e., by extracting features from EEG signals, and by using a classifier to identify the user’s mental state from...
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ژورنال
عنوان ژورنال: DEStech Transactions on Engineering and Technology Research
سال: 2017
ISSN: 2475-885X
DOI: 10.12783/dtetr/ameme2016/5793