Panorama: An enabling technology for Hearables

Abstract

The emergence of in-ear wearable sensors, so called Hearables, offers a convenient and cost-effective tool for long-term health monitoring. Traditionally, extracting frequency domain features from these signals relies on Power Spectral Density (PSD), which is highly susceptible to the effects of stochastic noise. This poses a formidable challenge for the processing of in-ear biosignals, which often have low signal-to-noise ratios (SNR). To address this problem, this work employs our recently proposed method for spectral analysis, termed "Panorama", defined as the Fourier transform of the autoconvolution. Experiments on in-ear EEG signals with extremely low SNR and in-ear sleep classification tasks show that, compared with the widely-used PSD, the Panorama spectrum offers better separability of features. We also show that a time series can be reconstructed from its autoconvolution, the inverse Fourier transform of Panorama, offering an additional analysis tool for time-domain applications.