Novel software for continuous wavelet analysis enable EEG real-time analysis on portable computers

TL;DR

This paper introduces a Python-based software for continuous wavelet transform (CWT) that enables real-time EEG analysis on portable computers, facilitating lightweight brain-machine interfaces and other waveform analysis applications.

Contribution

The study develops and demonstrates a fast, flexible CWT software optimized for small, portable computers, advancing real-time EEG analysis capabilities.

Findings

Achieved real-time EEG analysis speed on portable computers.

Software supports various CWT parameters for flexibility.

Potential to enhance lightweight brain-machine interfaces.

Abstract

Continuous Wavelet Transform (CWT) is frequently used for waveform analysis. For example, in the field of neuroscience research, CWT is performed to analyze electroencephalograms (EEG) and calculate the index of brain activity. Recent advancements in computer technology, such as general-purpose computing on Graphics Processing Units (GPGPU), have enabled the application of CWT to real-time waveform analysis. However, the computational complexity of CWT is large, and it is challenging to employ CWT as a real-time analysis method, such as in brain-machine interfaces (BMI), which require small size and cost. Therefore, a fast calculation method suitable for small and lightweight computers is desired. In this study, Python-based software for the CWT was developed and tested on portable computers. Using this software, real-time analysis of 64-electrode EEG data based on CWT was simulated and demonstrated adequate speed for the real-time analysis. Furthermore, it exhibited flexibility in performing CWT with various parameters. This software can contribute to the development of compact and lightweight BMI devices. Since CWT is a mathematical method, it may be used as a tool for other purposes.