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The common spatial patterns (CSP) algorithm has been widely used in EEG classification and brain computer interface (BCI). In this paper, we propose a multilinear formulation of the CSP, termed as TensorCSP or common tensor discriminant analysis (CTDA) for high-order tensor data. As a natural extension of CSP, the proposed algorithm uses the analogous optimization criteria in CSP and a new framework...
A major challenge in applying machine learning methods to Brain-Computer Interfaces (BCIs) is to overcome the on-line non-stationarity of the data blocks. An effective BCI system should be adaptive to and robust against the dynamic variations in brain signals. One solution to it is to adapt the model parameters of BCI system online. However, CSP is poor at adaptability since it is a batch type algorithm...
In order to verify whether or not the EEG patterns can be classified when the subjects perceive different types of geometric figures, we perform some EEG experiments. In this paper, the evoked potentials by three types of geometric figures are extracted and classified using a series of approaches. First, a two-stage source extraction algorithm is proposed to extract the evoked potentials from the...
Automatic recognition of cardiac arrhythmias is important for diagnosis of cardiac abnormalities. This paper presents a new approach to classification ECG signals based on feature extraction to diagnose heartbeat irregularities. We introduce the independent component analysis (ICA) feature extraction method and propose an over-complete feature extraction method combining ICA basis function's coefficients...
The paper investigates DNA reads classification based on transition matrix before assembling reads into configs in sequencing microbial communities directly sampled from natural environment. Traditional methods use dynamic programming algorithm to directly detect overlaps between reads in order to find out whether or not two reads can be assembled into one config. However, in microbial communities,...
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