UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Signal Extraction for Brain-Computer Interface

Hardoon, D; Shawe-Taylor, J; (2003) Signal Extraction for Brain-Computer Interface. In: (Proceedings) NIPS 2003 Workshop on 'Machine Learning Meets the User Interface'.

Full text not available from this repository.

Abstract

We use Kernel Canonical Correlation Analysis (KCCA) for detecting brain activity in function MRI by learning a semantic representation of fMRI brain scans and their associated time frequency. The semantic space provides a common representation and enables a comparison between the fMRI and time frequency. We compare the approach against Canonical Correlation Analysis (CCA) by localising brain regions that control finger movement and regions that are involved in mental calculation. We also compare the two approaches on a simulated null data set. We hypothesis that once a link can be established between regions of the brain to task one could create a brain-computer interface were computer related tasks could be activated by brain "thought" activity

Type:Proceedings paper
Title:Signal Extraction for Brain-Computer Interface
Event:NIPS 2003 Workshop on 'Machine Learning Meets the User Interface'
Keywords:Canonical correlation analysis, Correlation Analysis, fMRI, KCCA, kernel canonical correlation analysis
UCL classification:UCL > School of BEAMS > Faculty of Engineering Science > Computer Science

Archive Staff Only: edit this record