Independent Component Analysis and Signal Separation is one of the most exciting current areas of research in statistical signal processing and unsupervised machine learning. The area has received attention from several research communities including machine learning, neural networks, statistical signal processing and Bayesian modeling. independent Component Analysis and Signal Separation has applications at the intersection of many science and engineering disciplines concerned with understanding and extracting useful information from data as diverse as neuronal activity and brain images, bioinformatics, communications, the world wide web, audio, video, sensor signals, or time series.

Previous conferences in this series were held in Aussois (France), Helsinki (Finland), San Diego (CA, USA), Nara (Japan), Granada (Spain) and Charleston (SC, USA). This year the 7th International Conference on Independent Component Analysis and Signal Separation, ICA 2007, will be held at Queen Mary, University of London, UK, from Sunday 9 September to Wednesday 12 September 2007. ICA 2007 is organized and sponsored by the ICA Research Network, an EPSRC-funded network of researchers in over 25 UK institutions.

The conference will include invited talks as well as oral and poster presentations of refereed papers. It will be organized in a single track and will be selective. The main conference (Mon-Wed 10-12 Sept 2007) will be preceded by a half day of Tutorials (Sun 9 Sept 2007).

The proceedings of ICA 2007 will be published in Springer-Verlag’s Lecture Notes in Computer Science Series (LNCS), and will be available in printed version and electronically via LNCS Online.

ICA 2007 is organized by the UK ICA Research Network.

Keynote Speakers
> Scott Makeig, Swartz Center for Computational Neuroscience, Institute for Neural Computation, UCSD
> Shoji Makino, NTT Communication Science Laboratories, Kyoto, Japan