Advances in Cognitive Neural Prosthesis: Recognition of Neural Data with an Information-Theoretic Objective

We give an overview of recent advances in cognitive-based neural prostheses, and point out the major differences with respect to commonly used motor-based brain-machine interfaces. While encouraging results in neuroprosthetic research have demonstrated the proof of concept, the development of practical neural prostheses is still in the phase of infancy. To address complex issues arising in the development of practical neural prostheses we review several related studies ranging from the identification of new cognitive variables to the development of novel signal processing tools.

In the second part of this chapter, we discuss an information-theoretic approach to the extraction of low-dimensional features from high-dimensional neural data. We argue that this approach may be better suited for certain neuroprosthetic applications than the traditionally used features. An extensive analysis of electrical recordings from the human brain demonstrates that processing data in this manner yields more informative features than off-the-shelf techniques such as linear discriminant analysis. Finally, we show that the feature extraction is not only a useful dimensionality reduction technique, but also that the recognition of neural data may improve in the feature domain.