Bioelectrical signals are increasingly acquired at scales that challenge the bandwidth of brain-computer interfaces. However, their compression is still often framed as a problem of waveform preservation, limited by the entropy of the raw signal. Here we propose an information-theoretic framework in which the effective information of bioelectrical data is determined not only by signal fidelity, but also by physiological structure, model capacity and downstream task requirements. We formulate bioelectrical compression as a three-level hierarchy. At the signal level, noise is reduced to the information they carry about latent physiological sources. At the physiological level, parametric encoders map purified signals into compact, structured and quantized representations. At the semantic level, task-irrelevant information is discarded, while deep learning models exploit causal dependencies to replace marginal entropy with conditional entropy. This perspective reframes the compression limit of bioelectrical signals as a model- and task-conditioned quantity rather than a fixed property of the waveform. As increasingly expressive models become integrated with neural and physiological interfaces, bioelectrical compression may shift from transmitting signals to transmitting only the residual information required for task-level interpretation.

翻译：生物电信号以日益增长的规模被采集，这对脑机接口的带宽构成了挑战。然而，其压缩问题常被局限于波形保真度的框架内，受限于原始信号的熵值。本文提出一个信息论框架，在该框架中，生物电数据的有效信息不仅由信号保真度决定，还受生理结构、模型能力及下游任务需求的影响。我们将生物电压缩定义为三个层级：在信号层面，噪声被削减至其携带的关于潜在生理源的信息；在生理层面，参数化编码器将净化后的信号映射为紧凑、结构化且量化的表征；在语义层面，与任务无关的信息被舍弃，同时深度学习模型利用因果依赖关系，以条件熵替代边际熵。该视角将生物电信号的压缩极限重新定义为一种依赖于模型和任务的量，而非波形的固有属性。随着表达能力日益增强的模型与神经及生理接口相融合，生物电压缩或将从传输信号转向仅传输任务级解释所需的残差信息。