Classifying EEG for Brain-Computer Interface: Learning Optimal Filters for Dynamical System Features
نویسندگان
چکیده
منابع مشابه
Classifying EEG for Brain-Computer Interfaces: Learning Optimal Filters for Dynamical System Features
Classification of multichannel EEG recordings during motor imagination has been exploited successfully for brain-computer interfaces (BCI). In this paper, we consider EEG signals as the outputs of a networked dynamical system (the cortex), and exploit novel features from the collective dynamics of the system for classification. Herein, we also propose a new framework for learning optimal filter...
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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...
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Common spatial pattern (CSP) is very successful in constructing spatial filters for detecting event-related synchronization and event-related desynchronization. In statistics, a CSP filter can optimally separate the motor-imagery-related components. However, for a single trail, the EEG features extracted after a CSP filter still include features not related to motor imagery. In this study, we i...
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ژورنال
عنوان ژورنال: Computational Intelligence and Neuroscience
سال: 2007
ISSN: 1687-5265,1687-5273
DOI: 10.1155/2007/57180