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Lecture
Neural Signals and Signal Processing
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Related lectures (31)
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Neural Signals and Signal Processing
Explores neural signal processing for brain-computer interfaces, including decoding techniques like Kalman filters and spike sorting.
Discrete Fourier Transform: Unlimited Duration Signals
Explores the Discrete Fourier Transform applied to signals of unlimited duration using various windows for improved accuracy.
Signal Processing: Sampling and Reconstruction
Covers the concepts of quantization, coding, and sampling in signal processing.
Fourier Transform: Derivatives and Laplace Transform
Explores the Fourier transform properties with derivatives and introduces the Laplace transform for signal transformation and solving differential equations.
Untitled
Fourier Transform Interpretation and Reciprocity Theorem
Explores the interpretation of Fourier transform and its application in signal modification.
Fourier Transform: Basics and Applications
Covers the basics of the Fourier transform and its applications in signal processing.
Signal Processing: Sampling and Reconstruction
Covers Fourier transform, sampling, reconstruction, Nyquist frequency, and ideal signal reconstruction.
Sampling and Reconstruction Theory
Covers the concepts of analog, discrete, and digital signals, sampling times, frequencies, and pulses.
Signal Representations
Covers the properties of signal/data representations using Fourier transformations and matrices.
