Towards Analysis-aware EEG Compression in Wearable Computing

EEG signals are known for their high temporal resolution and data volume, posing challenges in storage, transmission, and analysis. With the increasing prevalence of wearable mobile BCI devices replacing large medical-grade recording devices, the need for efficient on-device processing and wireless transmission has grown. We propose a novel analyzability-aware EEG compression scheme optimized for machine consumption, facilitating efficient, real-time wearable processing while allowing reconstruction for detailed analysis and human understanding. Our method employs a wavelet-based compression technique that considers the Compression Factor, reconstruction error, and the impact of compression on on-device processing and data transmission. Experimental results using publicly available datasets demonstrate that machine learning models trained on compressed data using our method achieve performance comparable to the winning solution of BCI Competition II. Moreover, our approach offers multiple compression configurations suitable for various wearable computing scenarios.

I. Introduction

Electroencephalography (EEG) is a non-invasive technique used to measure the brain's electrical activity. EEG signals are widely used in various applications, including brain-computer interfaces (BCI), which allow individuals to control external devices using their brain activity. EEG signals are typically characterized by high temporal resolution and data volume, which can pose challenges in terms of data storage, transmission, and analysis. As EEG signal acquisition becomes more widespread, transitioning from huge medical-grade recording devices to wearable mobile BCI devices the need for efficient on-device processing and wireless transmission has become increasinzly important.