Electroencephalographic (EEG) recordings are contaminated by instrumental, environmental, and biological arti-facts, resulting in low signal-to-noise ratio. Artifact detection is a critical task for real-time applications where the signal is used to give a continuous feedback to the user. In these applications, it is therefore necessary to estimate online a signal quality index (SQI) in order to stop the feedback when the signal quality is unacceptable. In this article we introduce the Riemannian Potato Field (RPF) algorithm as such SQI. It is a generalization and extension of the Riemannian Potato, a previously published real-time artifact detection algorithm, whose performance is degraded as the number of channels increases. The RPF overcomes this limitation by combining the outputs of several smaller potatoes into a unique SQI resulting in a higher sensitivity and specificity, regardless of the number of electrodes. We demonstrate these results on a clinical dataset totalizing more than 2200 hours of EEG recorded at home, that is, in a non-controlled environment.