EEG的信息容量：可恢复神经信息的理论与计算极限
Information Capacity of EEG: Theoretical and Computational Limits of Recoverable Neural Information
AI总结：
该研究结合信息论与合成正向建模量化头皮EEG的互信息，揭示电极数、SNR与解码器性能的根本限制。
摘要：
脑电图（EEG）被广泛用于研究人类脑动力学，但其定量信息容量仍不清楚。在此，我们结合信息论与合成正向建模，以估计潜在皮层源与EEG记录之间的互信息。利用高斯信道理论和经验模拟，我们发现头皮EEG关于低维神经活动每个样本仅传递数十比特。信息在约64–128个电极时趋于饱和，并随信噪比（SNR）呈对数缩放。线性解码器捕获了几乎所有可线性恢复的方差，但其恢复的互信息仍远低于解析信道容量，这表明主导限制在于测量物理过程，而非算法复杂度。这些结果勾勒出可从EEG推断脑状态或思维内容结构多少信息的内在上限。
英文摘要：
Electroencephalography (EEG) is widely used to study human brain dynamics, yet its quantitative information capacity remains unclear. Here, we combine information theory and synthetic forward modeling to estimate the mutual information between latent cortical sources and EEG recordings. Using Gaussian-channel theory and empirical simulations, we find that scalp EEG conveys only tens of bits per sample about low-dimensional neural activity. Information saturates with approximately 64-128 electrodes and scales logarithmically with signal-to-noise ratio (SNR). Linear decoders capture nearly all variance that is linearly recoverable, but the mutual information they recover remains far below the analytic channel capacity, indicating that measurement physics - not algorithmic complexity - is the dominant limitation. These results outline the intrinsic ceiling on how much structure about brain state or thought content can be inferred from EEG.
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