Signal Transformation for Effective Multi-Channel Signal Processing

TL;DR

This paper introduces a bi-directional signal transformation method that converts multi-channel EEG signals into a single high-bandwidth channel, enabling the use of pre-trained single-channel models for efficient multi-channel analysis.

Contribution

It proposes a novel bi-directional signal transformation technique that simplifies multi-channel EEG processing by converting it into a single-channel problem, facilitating the use of existing models.

Findings

Effective transformation of EEG signals into high-bandwidth single-channel signals.

Preservation of information allowing inverse transformation without loss.

Improved efficiency in multi-channel EEG analysis.

Abstract

Electroencephalography (EEG) is an non-invasive method to record the electrical activity of the brain. The EEG signals are low bandwidth and recorded from multiple electrodes simultaneously in a time synchronized manner. Typical EEG signal processing involves extracting features from all the individual channels separately and then fusing these features for downstream applications. In this paper, we propose a signal transformation, using basic signal processing, to combine the individual channels of a low-bandwidth signal, like the EEG into a single-channel high-bandwidth signal, like audio. Further this signal transformation is bi-directional, namely the high-bandwidth single-channel can be transformed to generate the individual low-bandwidth signals without any loss of information. Such a transformation when applied to EEG signals overcomes the need to process multiple signals and allows for a single-channel processing. The advantage of this signal transformation is that it allows the use of pre-trained single-channel pre-trained models, for multi-channel signal processing and analysis. We further show the utility of the signal transformation on publicly available EEG dataset.