zpybci 0.2.0

High-performance signal processing for BCI research

zpybci -- Zerostone Python for BCI

High-performance, real-time signal processing for brain-computer interface research. Powered by Rust with zero-copy NumPy integration.

Installation

pip install zpybci

Wheels are available for Linux (x86_64, aarch64), macOS (Intel + Apple Silicon), and Windows (x86_64). Python 3.8+.

Quick Start

import numpy as np
import zpybci as zbci

# Bandpass filter for alpha band (8-12 Hz)
bpf = zbci.IirFilter.butterworth_bandpass(sample_rate=256.0, low_cutoff=8.0, high_cutoff=12.0)
signal = np.random.randn(1000).astype(np.float32)
filtered = bpf.process(signal)

# Chain multiple stages into a pipeline
pipe = zbci.Pipeline(sample_rate=256.0, stages=[
    ("highpass", {"cutoff": 1.0}),
    ("notch", {"freq": 50.0}),
    ("lowpass", {"cutoff": 40.0}),
])
cleaned = pipe.process(signal)

Features

Filters

• IIR -- 4th-order Butterworth (lowpass, highpass, bandpass)
• FIR -- arbitrary-length finite impulse response
• AC coupling -- DC removal for streaming data
• Median -- nonlinear smoothing
• Adaptive -- LMS and NLMS for noise cancellation
• Notch -- narrowband rejection (e.g., 50/60 Hz line noise)

Spatial Filters

• CAR -- common average reference
• Surface Laplacian -- current source density approximation
• Channel Router -- flexible channel remapping

Spectral Analysis

• FFT -- fast Fourier transform (magnitude/phase)
• STFT -- short-time Fourier transform
• Multi-band power -- concurrent power in multiple frequency bands
• Welch PSD -- power spectral density estimation
• CWT -- continuous wavelet transform (Morlet)

Detection

• Threshold -- fixed-threshold event detection
• Adaptive threshold -- self-adjusting threshold based on signal statistics
• Zero-crossing -- rate estimation

Artifact Handling

• Amplitude-based -- flag samples exceeding a threshold
• Z-score -- flag statistically outlying segments

Analysis

• Envelope follower -- instantaneous amplitude via rectification + smoothing
• Windowed RMS -- streaming root-mean-square
• Hilbert transform -- analytic signal, instantaneous phase/frequency

Statistics

• Online mean/variance -- Welford's algorithm, no buffer required
• Online covariance -- streaming covariance matrix

Advanced

• CSP -- common spatial patterns with online adaptation
• Riemannian tangent space -- SPD manifold projection for classification
• OASIS deconvolution -- calcium transient inference from fluorescence traces

Utilities

• Pipeline -- declarative stage chaining with a single process() call
• Resampling -- integer decimation and interpolation
• Streaming percentile -- approximate quantiles on unbounded streams
• Clock sync -- offset estimation, linear drift correction, sample clock alignment
• Cross-correlation -- full, valid, and circular modes
• Window functions -- Hann, Hamming, Blackman, flat-top, Kaiser

Version

0.2.0

License

AGPL-3.0