Introducing a Deflationary Approach to Space-Time ICA that uses temporal methods in Brain Signals Processing.

Abstract

For the extraction of underlying sources of brain activity, time structure-based techniques for applying Independent Component Analysis (ICA) have been demonstrably more robust than state-of-the-art statistical-based methods, such as FastICA. Since the early application of conventional ICA on electroencephalogram (EEG) recordings, Space-Time ICA (ST-ICA) has emerged as more capable approach for extracting complex underlying activity, but not without the 'curse of dimensionality'. The challenges in the future development of ST-ICA will require a focus on the optimisation of the mixing matrix, and on component clustering techniques. This paper proposes a new optimisation approach for the mixing matrix, which makes ST-ICA more tractable, when using a time structure-based ICA technique, LSDIAG. Such techniques rely on constructing a multi-layer covariance matrix, Cxk of the original dataset to generate the inverse of the mixing matrix; Csk = WCxkWT. This means a simple truncation of the mixing matrix is not appropriate. To overcome this, we propose a deflationary approach to optimise a much smaller mixing matrix - based on the absolute values of the diagonals of the co-variance matrix, Csk, to represent the underlying sources. The preliminary results of the new technique applied to different channels of EEG recorded using the standard 10-20 system - including the full selection of all channels - are very promising.Clinical Relevance-The potential of this deflationary approach for Space-Time ICA, seeks to allow clinicians to identify underlying sources in the brain - that both spatially and spectrally overlap - to be identified, whilst making the 'dimensionality' challenges more tractable. In the long run, applications of this technique could enhance certain brain-computer interface paradigms.