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THE PROBLEM

THE PROBLEM. TO CLASSIFY EEG SIGNALS USING WAVELET TRANSFORMS AND NEURAL NETWORKS. A SOLUTION. The Brain. A Neuron Cell. Electrode Placement. Discrete Fourier Transform. Time-Frequency Plane. y(t)=f(t). Short Time Fourier Transform. Heisenberg Principle:. A ‘Sliding Window’.

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THE PROBLEM

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  1. THE PROBLEM • TO CLASSIFY EEG SIGNALS USING WAVELET TRANSFORMS AND NEURAL NETWORKS ISSC DIT Kevin St.

  2. A SOLUTION ISSC DIT Kevin St.

  3. The Brain • A Neuron Cell ISSC DIT Kevin St.

  4. Electrode Placement ISSC DIT Kevin St.

  5. Discrete Fourier Transform Time-Frequency Plane y(t)=f(t) ISSC DIT Kevin St.

  6. Short Time Fourier Transform Heisenberg Principle: ISSC DIT Kevin St.

  7. A ‘Sliding Window’ ISSC DIT Kevin St.

  8. A Windowed EEG Signal ISSC DIT Kevin St.

  9. Spectrogram of the Mathematics task EEG signal ISSC DIT Kevin St.

  10. A 3-D Spectrogram of an EEG signal ISSC DIT Kevin St.

  11. Time-Frequency and Corresponding Basis Function ISSC DIT Kevin St.

  12. Logarithmic Tree ISSC DIT Kevin St.

  13. Wavelet Transform implementation • using Subband Coding ISSC DIT Kevin St.

  14. An example of a Pruning cost analysis Prune if M(Parent)>M(child1)+M(child2) ISSC DIT Kevin St.

  15. Best Tree Structure for the Mathematics Task ISSC DIT Kevin St.

  16. The Wavelet Packet Transform Time-Frequency Plane ISSC DIT Kevin St.

  17. Wavelet Coefficients: Augmented Time Format ISSC DIT Kevin St.

  18. Neural Network Structure ISSC DIT Kevin St.

  19. Recognition rate results for the WPT ISSC DIT Kevin St.

  20. Double-Sideband Suppressed Carrier:Reconstruction of the Wavelet Coefficients ISSC DIT Kevin St.

  21. A Designer Wavelet ISSC DIT Kevin St.

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