An efficient discriminant-based solution for small sample size problem
We present a systematic technique for extraction of useful information from large-scale neural data in the context of brain-computer interfaces. The technique is based on a direct linear discriminant analysis, recently developed for face recognition problems. We show that this technique is capable of extracting useful information from brain data in a systematic fashion and can serve as a general analytical tool for other types of biomedical data, such as images and collections of images (movies). The performance of the method is tested on intracranial electroencephalographic data recorded from the human brain.