Abstract
Brain computer interfaces (BCIs) have been attracting a great interest in recent years. The common spatial patterns (CSP) technique is a well-established approach to the spatial filtering of the electroencephalogram (EEG) data in BCI applications. Even though CSP was originally proposed from a heuristic viewpoint, it can be also built on very strong foundations using information theory. This paper reviews the relationship between CSP and several information-theoretic approaches, including the Kullback–Leibler divergence, the Beta divergence and the Alpha-Beta log-det (AB-LD)divergence. We also revise other approaches based on the idea of selecting those features that are maximally informative about the class labels. The performance of all the methods will be also compared via experiments.
Highlights
The electroencephalogram (EEG) is a record over time of the differences of potential that exist between different locations on the surface of the head [1,2]
The results reveal that, in general and except in a few isolated cases, the null hypothesis that the other methods do not significantly improve the performance over common spatial patterns (CSP) cannot be discarded. (a) Average accuracy obtained by the algorithms for each subject; (b) p-values of the t-tests that compare whether the performance of the alternative algorithms is significantly better than the one obtained by CSP
We have reviewed several information theoretic approaches for motor-imagery
Summary
The electroencephalogram (EEG) is a record over time of the differences of potential that exist between different locations on the surface of the head [1,2]. Brain-computer interfaces (BCIs) are computer-based systems that enable us to control a device with the mind, without any muscular intervention [3,4,5,6]. This technology, though not yet mature, has a number of therapeutic applications, such as the control of wheelchairs by persons with severe disabilities, and finds use in fields as diverse as gaming, art or access control. The imagined actions are translated into different device commands (e.g., when the subject imagines the motion of the left hand, the wheelchair is instructed to turn to the left)
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