Noboru Ohnishi

Extraction of Sleep-Spindles from the Electroencephalogram (EEG)

Independent component analysis (ICA) is a powerful tool for separating signals from their observed mixtures. This area of research has produced many varied algorithms and approaches to the solution of this problem. The majority of these methods adopt a truly blind approach and disregard available a priori information in order to extract the original sources or a specific desired signal. In this contribution we propose a fixed point algorithm which utilizes a priori information in finding a specified signal of interest from the sensor measurements. This technique is applied to the extraction and channel isolation of sleep spindles from a multi-channel electroencephalograph (EEG).

An algorithm based on the even moments of the error

We propose an algorithm based on a linear combination of the even moments of the error for adaptive filtering, called weighted even moment (WEM) algorithm. It is similar to the well-known least mean square (LMS) and to the family of algorithms proposed by Walach and Widrow (1994). This later ones were shown to behave poorer than the LMS, however, when the noise was Gaussian. We study the WEM algorithm convergence behavior and deduce equations for the misadjustment and the learning time. The results showed that the WEM had better performance than the LMS when the noise had a Gaussian distribution.