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Step 2: LFM signal classification.

Fig. 4 General process of EEG signal classification.

And then the output detected signal is considered as a signal classification attribute.

The approach is based on the high-resolution technique known as multiple signal classification (MUSIC).

Extensive experiments are conducted on image and EEG signal classification to evaluate the effectiveness of DMELM.

This paper introduces a signal classification framework that can be used for brain computer interface design.

This paper proposes a two-stage feed forward neural network for ECG signal classification.

This paper addresses the problem of feature extraction for signal classification.

Wavelets have proved particularly effective for extracting discriminative features in ECG signal classification.

Signal classification is thereafter accomplished by a minimum-distance rule with noteworthy results.

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