Objective. Unobtrusive electroencephalography (EEG) monitoring in everyday life requires the availability of highly miniaturized EEG devices (mini-EEGs), which ideally consist of a wireless node with a small scalp area footprint, in which the electrodes, amplifier and wireless radio are embedded. By attaching a multitude of mini-EEGs at relevant positions on the scalp, a wireless ‘EEG sensor network’ (WESN) can be formed. However, each mini-EEG in the network only has access to its own local electrodes, thereby recording local scalp potentials with short inter-electrode distances. This is unlike using traditional cap-EEG, which by the virtue of re-referencing can measure EEG across arbitrarily large distances on the scalp. We evaluate the implications and limitations of such far-driven miniaturization on neural decoding performance. Approach. We collected 255-channel EEG data in an auditory attention decoding (AAD) task. As opposed to previous studies with a lower channel density, this new high-density dataset allows emulation of mini-EEGs with inter-electrode distances down to 1 cm in order to identify and quantify the lower bound on miniaturization for EEG-based stimulus decoding. Main results. We demonstrate that the performance remains reasonably stable for inter-electrode distances down to 3 cm, but decreases quickly for shorter distances if the mini-EEG nodes can be placed at optimal scalp locations and orientations selected by a data-driven algorithm. Significance. The results indicate the potential for the use of mini-EEGs in a WESN context for AAD applications and provide guidance on inter-electrode distances while designing such devices for neuro-steered hearing devices.

客观的. 日常生活中不显眼的脑电图 (EEG) 监测需要高度微型化的 EEG 设备 (mini-EEG) 的可用性，理想情况下，该设备由具有小头皮面积的无线节点组成，其中嵌入了电极、放大器和无线电。通过在头皮上的相关位置附加大量迷你脑电图，可以形成无线“脑电图传感器网络”（WESN）。然而，网络中的每个微型脑电图只能访问自己的局部电极，从而以较短的电极间距离记录局部头皮电位。这与使用传统的 cap-EEG 不同，它通过重新参考可以测量头皮上任意大距离的 EEG。我们评估了这种远驱动小型化对神经解码性能的影响和局限性。方法。我们在听觉注意力解码 (AAD) 任务中收集了 255 通道 EEG 数据。与之前具有较低通道密度的研究相反，这个新的高密度数据集允许模拟电极间距离低至 1 cm 的微型 EEG，以便识别和量化基于 EEG 的刺激解码的小型化下限。主要结果。我们证明，对于低至 3 cm 的电极间距离，性能保持相当稳定，但如果可以将迷你 EEG 节点放置在由数据驱动算法选择的最佳头皮位置和方向，则性能会在较短距离内迅速下降。意义. 结果表明在 AAD 应用的 WESN 环境中使用微型脑电图的潜力，并在为神经导向听力设备设计此类设备时提供有关电极间距离的指导。