Signal Transformation for Effective Multi-Channel Signal Processing
This addresses the need for more efficient EEG signal processing for researchers and clinicians, though it appears incremental as it builds on basic signal processing techniques.
The paper tackles the problem of processing multi-channel EEG signals by proposing a bi-directional signal transformation that combines multiple low-bandwidth channels into a single high-bandwidth channel, enabling the use of pre-trained single-channel models without information loss.
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.