Title :
Desynchronization Network Analysis for the Recognition of Imagined Movement
Author_Institution :
Sch. of Information Tech., Sydney Univ., NSW
Abstract :
This paper reports on the use of electroencephalogram (EEG)-based phase desynchronization networks for the recognition of imagined movements. Features derived solely from these networks are classified using linear support vector machine. An average accuracy of 73% is achieved for the single-trial imagined hand versus foot movements. The results demonstrate that phase desynchronizations provide relevant information for the discrimination of mental tasks. This novel approach will potentially benefit the development of brain-computer interfaces
Keywords :
electroencephalography; handicapped aids; medical signal processing; signal classification; support vector machines; EEG; brain-computer interfaces; electroencephalogram; feature classification; foot movement; hand movement; imagined movement recognition; linear support vector machine; mental tasks; phase desynchronization network analysis; Brain modeling; Electroencephalography; Foot; Frequency synchronization; Image analysis; Image recognition; Signal processing; Signal resolution; Spatiotemporal phenomena; Statistics;
Conference_Titel :
Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the
Conference_Location :
Shanghai
Print_ISBN :
0-7803-8741-4
DOI :
10.1109/IEMBS.2005.1616871