neural-signal-processing

A list of open resources for learning and working with digital signal processing.

MATLAB toolbox for SSVEP-based BCI: 19 template-based frequency detection methods including CCA, TRCA, CORRCA, MSI, and multiset CCA, with benchmark code and demos.

Simulation first governed neuroadaptive runtime for BCI inspired intent to action systems: uncertainty aware abstention, signal quality and drift detection, locally qualified decoders, shared autonomy, symbolic world modeling, Simplex fallback, evidence gated adaptation, signed replayable assurance, and deterministic fault injection.

Python code for the Neural Signal Processing and Analysis Course given by Mike X Cohen.

Neuro-inspired machine learning on intracortical recording. The aim is to build useful and compressed local field potential features from spike times as part of the 2023 OHBM hackathon.

Neural Decoder Optimization: A Comparative Study of Modern Optimizers

Classifies rock-paper-scissors hand gestures from optically pumped magnetometer (OPM) data recorded at the University of Nottingham.

Comparison of machine learning architectures for neural decoding of self-location | MSc dissertation project

Causal neural telemetry decoding, probabilistic forecasting, signal processing, statistics, and closed-loop BCI systems research.

Implements EEG-based neurofeedback experiments with PsychoPy. Analyzes neural activity for performance changes.

The project aims to use an interdisciplinary approach to test if neural processes in occulomotor and motor areas could be studies using mathematical models from neck and shoulder muscles during cognitive tasks

Predicts people’s stress levels from their resting-state EEG recordings in the LEMON dataset.

brain computer interface notes

From-scratch Python implementations of Common Spatial Pattern (CSP) and Common Spatio-Spectral Pattern (CSSP) for EEG-based motor imagery brain-computer interfaces (MI-BCI), tested on BCI Competition III Dataset IVa.