Software
nSTAT — Neural Spike Train Analysis Toolbox
nSTAT is an open-source toolbox for neural spike train data analysis built in MATLAB. It implements a comprehensive suite of models and algorithms with a focus on point-process generalized linear models (GLMs), model fitting, model-order analysis, and adaptive decoding. In addition to point-process algorithms, nSTAT provides tools for Gaussian signals — from correlation analysis to the Kalman filter — applicable to continuous neural signals such as LFP, EEG, and ECoG.
Key capabilities include state-space GLM (SSGLM) estimation via EM, unscented Kalman filtering (UKF), goal-directed point-process adaptive filters (PPAF), and hybrid discrete/continuous point-process filters (PPHF). Although created with neural signal processing in mind, nSTAT can be used as a generic tool for analyzing any types of discrete and continuous signals.
Citation: Cajigas I, Malik WQ, Brown EN. nSTAT: Open-source neural spike train analysis toolbox for Matlab. Journal of Neuroscience Methods 211: 245–264, 2012.
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nSTAT-python — Python Port of nSTAT
nSTAT-python is a full Python port of the nSTAT toolbox. It brings the complete functionality of the original MATLAB toolbox to the Python ecosystem, implementing the same point-process GLMs, model fitting, adaptive decoding, and signal processing algorithms. The Python port includes native implementations of all core algorithms along with extensive documentation, paper-example galleries, and unit tests.
The Python port was verified against the MATLAB reference through a comprehensive audit covering all 16 classes and 484 methods, ensuring full class-level and behavioral parity. nSTAT-python can be installed via pip (pip install nstat-toolbox) and includes built-in example datasets and interactive documentation.
Citation: Cajigas I, Malik WQ, Brown EN. nSTAT: Open-source neural spike train analysis toolbox for Matlab. Journal of Neuroscience Methods 211: 245–264, 2012.
View nSTAT-python on GitHub View Documentation View Publication
Thalamus — Real-Time Multimodal Data Capture Platform
Thalamus is an open-source Python program designed for real-time, synchronized, closed-loop multimodal data capture, specifically tailored for the demands of neurosurgical environments. Thalamus facilitates the advancement of clinical applications of Brain-Computer Interface (BCI) technology by integrating behavioral and electrophysiological data streams.
Thalamus prioritizes minimal operating-room setup, high reliability with fail-safe architecture guaranteeing minimal data loss, real-time computation for visualization of research and clinical data streams, and closed-loop control. It supports a high-bandwidth, low-latency, parallel distributed architecture for modular acquisition and computation that can be scaled over time. Thalamus is available for both Linux and Windows.