scitex-dsp

Digital signal-processing primitives for neuroscience — Hilbert analytic signal, power spectral density / band powers, phase-amplitude coupling and the modulation index, continuous wavelet transform, Buzsaki-style ripple detection, deterministic demo signals, and pre-/post-processing utilities (crop, resample, noise synthesis, segmentation). Extracted from the SciTeX ecosystem as a standalone package.

import scitex_dsp as dsp

xx, tt, fs = dsp.demo_sig(sig_type="chirp", fs=1024)
ana = dsp.hilbert(xx)

Contents:

• API
  • Functionalities
  • IO
  • Dependencies
  • Performance
  • hilbert()
  • psd()
  • band_powers()
  • pac()
  • wavelet()
  • modulation_index()
  • crop()
  • ensure_3d()
  • resample()
  • time()
  • demo_sig()
  • to_segments()
  • to_sktime_df()
  • detect_ripples()
  • get_eeg_pos()
  • Spectral Primitives
  • Pre-/Post-processing
  • Ripple Detection
  • Signal Synthesis
  • Audio / EEG I/O

Indices and tables

• Index

• Module Index

• Search Page