CONF chavarriaga:eusipco:2008/IDIAP Asynchronous detection and classification of oscillatory brain activity Chavarriaga, Ricardo Galán, Ferran Millán, José del R. EXTERNAL http://publications.idiap.ch/attachments/papers/2008/chavarriaga-eusipco-2008.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/chavarriaga:rr08-36 Related documents 16 European Signal Processing Conference 2008 IDIAP-RR 08-36 The characterization and recognition of electrical signatures of brain activity constitutes a real challenge. Applications such as Brain-Computer Interfaces (BCI) are based on the accurate identification of mental processes in order to control external devices. Traditionally, classification of brain activity patterns relies on the assumption that the neurological phenomena that characterize mental states is continuously present in the signal. However, recent evidence shows that some mental processes are better characterized by episodic activity that is not necessarily synchronized with external stimuli. In this paper, we present a method for classification of mental states based on the detection of this episodic activity. Instead of performing classification on all available data, the proposed method identifies informative samples based on the class sample distribution in a projected canonical feature space. Classification results are compared to traditional methods using both artificial data and real EEG recordings. REPORT chavarriaga:rr08-36/IDIAP Asynchronous detection and classification of oscillatory brain activity Chavarriaga, Ricardo Galán, Ferran Millán, José del R. EXTERNAL http://publications.idiap.ch/attachments/reports/2008/chavarriaga-idiap-rr-08-36.pdf PUBLIC Idiap-RR-36-2008 2008 IDIAP Published in 16 European Signal Processing Conference (EUSIPCO-2008,',','), August 2008 The characterization and recognition of electrical signatures of brain activity constitutes a real challenge. Applications such as Brain-Computer Interfaces (BCI) are based on the accurate identification of mental processes in order to control external devices. Traditionally, classification of brain activity patterns relies on the assumption that the neurological phenomena that characterize mental states is continuously present in the signal. However, recent evidence shows that some mental processes are better characterized by episodic activity that is not necessarily synchronized with external stimuli. In this paper, we present a method for classification of mental states based on the detection of this episodic activity. Instead of performing classification on all available data, the proposed method identifies informative samples based on the class sample distribution in a projected canonical feature space. Classification results are compared to traditional methods using both artificial data and real EEG recordings.