Experiments on Using combined short window bivariate autoregression for EEG classification

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

4 Citations (Scopus)


In EEG-based classification problem, most of currently used features are univariate and extracted from single channels. However EEG signals recorded from multiple channels for a brain activity are correlated, features extracted from the EEG signals should reflect relationships among those channels. For this reason, we propose and apply a bivariate feature called Combined Short-Window BiVariate AutoRegres-sive model (CSWBVAR) for EEG classification problems. Given a pair of channels, we firstly divide each of them in to overlapping segments or short windows, and then estimate BVAR parameters for each pair of segments. CSWBVAR is formed by combining extracted BVAR parameters together with a pre-defined overlapping window parameter. We analyzed and compared CSWBVAR feature and univariate feature using the dataset III for motor imagery problem of BCI Competition II (2003). Preliminary results show that using CSWBVAR feature can improve classification accuracy up to 7% comparing with using univariate one with the same linear-kernel SVM classifier
Original languageEnglish
Title of host publicationProceeding Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2011
EditorsMetin Akay
Place of PublicationBoston
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Print)9781424441402
Publication statusPublished - 2011
Event5th International IEEE EMBS Conference on Neural Engineering - Cancun, Cancun, Mexico
Duration: 27 Apr 20111 May 2011


Conference5th International IEEE EMBS Conference on Neural Engineering
Abbreviated titleNER 2011


Dive into the research topics of 'Experiments on Using combined short window bivariate autoregression for EEG classification'. Together they form a unique fingerprint.

Cite this