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Brain-machine interfaces (BMIs) use neural activity related to motion parameters to enable brain directly control external devices. Some linear and nonlinear decoding techniques have been used successfully to infer arm trajectory from neural data. Unfortunately, these One stage decoding techniques can hardly get high accuracy and low computational demands at the same time. Here we introduce a Two...
In order to reduce the relativity and improve the separability of prototype pattern vectors, a spectral-based synergetic network learning algorithm is proposed in this paper. The most attractive feature of the new method is that its complexity is linear with data dimension. To approximate the optimal cut and prevent instability due to information loss, all eigenvectors are used. The eigenvalues and...
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