The Sensitivity of Ear-EEG: Evaluating the Source-Sensor Relationship Using Forward Modeling

Ear-EEG allows to record brain activity in every-day life, for example to study natural behaviour or unhindered social interactions. Compared to conventional scalp-EEG, ear-EEG uses fewer electrodes and covers only a small part of the head. Consequently, ear-EEG will be less sensitive to some cortical sources. Here, we perform realistic electromagnetic simulations to compare cEEGrid ear-EEG with 128-channel cap-EEG. We compute the sensitivity of ear-EEG for different cortical sources, and quantify the expected signal loss of ear-EEG relative to cap-EEG. Our results show that ear-EEG is most sensitive to sources in the temporal cortex. Furthermore, we show how ear-EEG benefits from a multi-channel configuration (i.e. cEEGrid). The pipelines presented here can be adapted to any arrangement of electrodes and can therefore provide an estimate of sensitivity to cortical regions, thereby increasing the chance of successful experiments using ear-EEG.

Environmental dimensionality determines species coexistence

According to the competitive-exclusion principle, the number n of regulating variables describing a given community dynamics is an upper bound on the number of species (or types or morphs) that can coexist at equilibrium. On occasion, it is possible to reformulate a model with a lower number of regulating variables than appeared in the initial specification. We call the smallest number of such variables the dimension of the environmental feedback, or environmental dimension for short. For studying which species can invade a community, it is enough to know the sign of each species' long-term growth rate, i.e., invasion fitness. Therefore, different indicators of population growth - so-called fitness proxies, such as the basic reproduction number-are sometimes preferred. However, as we show, different fitness proxies may have different dimensions. Fundamental characteristics such as the environmental dimension should not depend on such arbitrary choices. Here, we resolve this difficulty by introducing a refined definition of environmental dimension that focuses on neutral fitness contours. On this basis, we show that this definition of environmental dimension is not only unambiguous, i.e., independent of the choice of fitness proxy, but also constructive, i.e., applicable without needing to assess an infinite number of possible fitness proxies. We then investigate how to determine environmental dimensions by analysing the two components of the environmental feedback: the impact map describing how a community's resident species affect the regulating variables and the sensitivity map describing how population growth depends on the regulating variables. The dimension of the impact map is lower than n when the set of feasible environments is of lower dimension than n, and the dimension of the sensitivity map is lower than n when not all n regulating variables affect the sign of population growth independently. While the minimum of the dimensions of the impact and sensitivity maps provides an upper bound on the environmental dimension, the combined effect of the two maps can result in an even lower environmental dimension, which happens when the sensitivity map is insensitive to some aspects of the impact map's image. To facilitate the applications of the framework introduced here, we illustrate all key concepts with detailed worked examples. In view of these results, we claim that the environmental dimension is the ultimate generalization of the traditional and widely used notions of the "number of regulating variables" or the "number of limiting factors", and is thus the sharpest generally applicable upper bound on the number of species that can robustly coexist in a community.