  Brain computer interface (BCI) is the only way for some special patients to
communicate with the outside world and provide a direct control channel between
brain and the external devices. As a non-invasive interface, the scalp
electroencephalography (EEG) has a significant potential to be a major input
signal for future BCI systems. Traditional methods only focus on a particular
feature in the EEG signal, which limits the practical applications of EEG-based
BCI. In this paper, we propose a algorithm for EEG classification with the
ability to fuse multiple features. First, use the common spatial pattern (CSP)
as the spatial feature and use wavelet coefficient as the spectral feature.
Second, fuse these features with a fusion algorithm in orchestrate way to
improve the accuracy of classification. Our algorithms are applied to the
dataset IVa from BCI complete \uppercase\expandafter{\romannumeral3}. By
analyzing the experimental results, it is possible to conclude that we can
speculate that our algorithm perform better than traditional methods.
