BioSQ: Frequency-Aware Neural Compression of Biosignals

Neural codecs have recently emerged as effective compression models for time-series data, offering a compelling alternative to classical algorithms. For the most part, modern neural codecs are designed to compress audio recordings, such as music and speech, with their architectures and quantization strategies targeted to those modalities. Therefore, extending neural codecs to other modalities poses specific challenges that require careful consideration. In this work, we evaluate the suitability of neural compressors to a variety of biosignals: scalp electroencephalogram (EEG), intracranial EEG, and electrocardiogram (ECG). In particular, we find that global fidelity metrics, commonly used to evaluate compression performance, are insufficient to fully characterize the reconstruction of biosignals. Remarkably, our analysis shows that global fidelity can obscure frequency-localized distortions with disproportionate effects on downstream accuracy. Guided by these findings, we systematically revisit the main components of neural compressors, evaluating alternative quantization strategies and encoder–decoder architectures, and combine them with classical information-theoretic insights. On the one hand, we observe that implicit quantization schemes outperform classical vector quantization. In fact, we introduce residual binary spherical quantization (RBSQ), which extends binary spherical quantization with multiple residual stages, and show that it notably improves fidelity. On the other hand, we find that asymmetric encoder-decoder architectures, while improving reconstruction of audio data, notably underperform on biosignals. Based on our thorough analysis, we introduce BioSQ as the state-of-the-art (SoTA) neural codec for biosignals. We further reduce the bitrate of BioSQ through entropy regularization and arithmetic coding, promoting stable and uniform symbol distributions. BioSQ outperforms SoTA neural biosignal compressors in reconstruction fidelity across 4 physiological datasets and compression ratios from 4× to 128×, reducing reconstruction error by up to 81%. These improvements are also reflected in downstream tasks, where BioSQ better preserves performance on seizure detection, motor-imagery classification, and QRS detection by up to 25% as compression becomes more restrictive. Overall, our results show that neural codec design choices developed for audio do not transfer uniformly to physiological signals, and establish BioSQ as an effective approach to general-purpose biosignal compression.