Adaptive Chirplet Transform (ACT) – CPU & GPU Reference Implementations

Overview

This repository provides CPU and GPU reference implementations of the Adaptive Chirplet Transform (ACT).

It includes:

• A CPU reference implementation (act.py) for correctness verification and reproducibility
• A GPU-accelerated implementation (act_gpu.py) leveraging NVIDIA CUDA (via CuPy) to accelerate dictionary generation and iterative decomposition

Both implementations include mathematically corrected normalization and expanded evaluation support, ensuring numerical consistency across backends.

The codebase is intended for:

• Reproducible ACT research
• Performance comparison between CPU and GPU pipelines

The GPU implementation has been previously validated and published in a conference setting. The CPU implementation has been updated to match the corrected mathematical formulation used in the GPU version. Optional CPU/GPU monitoring utilities are included to record power, memory, and utilization metrics.

This implementation is based on the original CPU code by amanb2000.

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Repository Structure

The repository currently includes four core modules:

• act.py- Implements the mathematically correct Adaptive Chirplet Transform
• act_gpu.py – Implements the Adaptive Chirplet Transform including GPU-accelerated dictionary generation and signal decomposition.
• monitoringclass.py – Provides CPU/GPU usage monitoring (power, memory, utilization), logging results to CSV for later inspection.
• run_act_example.py – Example pipeline for loading EEG data, preprocessing, applying ACT, and exporting results.

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Features

• CPU reference implementation (act.py) for correctness verification and reproducibility
• GPU-accelerated chirplet dictionary construction using CuPy (act_gpu.py)
• Iterative ACT decomposition with parameter refinement via SciPy optimization
• EEG preprocessing supported through MNE
• Optional CPU/GPU monitoring during runs
• Outputs results (parameters, coefficients, reconstruction errors) as CSV

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Requirements

• Python 3.9+
• NVIDIA GPU with CUDA support (tested with CUDA 12.x) (if doing act_gpu.py)
• Recommended: 8 GB+ VRAM for larger EEG datasets (if doing act_gpu.py)

Python Dependencies

All required packages are listed in requirements.txt.
Install them with:

pip install -r requirements.txt

Quick Start

This guide will help you set up and run the provided code examples quickly.

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1. Clone the Repository

git clone https://github.com/your-username/your-repo.git
cd your-repo

2. Prepare EEG data

Place .edf EEG files in the expected directory structure. Example path used in run_act_example.py:

ACT/Bitbrain/sub-1/eeg/sub-1_task-Sleep_acq-headband_eeg.edf

3. Run the example script

python run_act_example.py

4. Check outputs

Result Logs:

act_results_sub-1_optimized.csv

Monitoring logs:

cpu_monitoring.csv
gpu_monitoring.csv

Output Format

The output CSV contains:

Epoch	Params (tc, fc, logDt, c)	Coeffs	Error	Residue

Repository Structure

├── act.py                 # CPU reference implementation
├── act_gpu.py             # GPU-accelerated ACT (CuPy / CUDA)
├── monitoringclass.py     # CPU/GPU monitoring utilities
├── run_act_example.py     # Example EEG processing pipeline
├── requirements.txt       # Python dependencies
├── README.md              # Project documentation
└── LICENSE                # MIT License

Citation

Preliminary results of this work appeared in the
27th Annual Mersivity / Water-HCI Symposium Proceedings
(pp. 55–56), Zenodo, 2025.
👉 https://doi.org/10.5281/zenodo.16973160

A full version with extended profiling and evaluation has been published at
the International Conference on Sensing Technology (ICST) 2025. 👉 https://ieeexplore.ieee.org/document/11512436

If you use this repository (CPU or GPU code), please additionally cite: Nishant Kumar, Adaptive Chirplet Transform (ACT) – CPU/GPU Reference Implementation, GitHub repository, 2026.

Author

Nishant Kumar

Zenodo Preliminary Results

@inproceedings{Mersivity2025,
  editor    = {Steve Mann and Michael Condry and Nishant Kumar},
  title     = {27th Annual Mersivity / Water-HCI Symposium Proceedings},
  pages     = {55--56},
  year      = {2025},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.16973160},
  url       = {https://doi.org/10.5281/zenodo.16973160}
}

ICST Results

@INPROCEEDINGS{11512436,
  author={Kumar, Nishant and Mann, Steve},
  booktitle={2025 18th International Conference on Sensing Technology (ICST)}, 
  title={GPU-Accelerated Chirplet Transform: Scalable Runtime Profiling and Analysis}, 
  year={2025},
  volume={},
  number={},
  pages={1-6},
  keywords={Graphics processing units;Timing;Central Processing Unit;Memory;Dictionaries;Testing;Transforms;Electroencephalography;Modeling;Printing;Chirplet Transform;Hardware Acceleration;GPGPU;Unified Memory;Time-Frequency Analysis;EEG Signal Processing;Signal Decomposition},
  doi={10.1109/ICST66402.2025.11512436}}

License

This project is released under the MIT License.