Spatial EEG filters are widely used to isolate event-related potential (ERP) components. The most commonly used spatial filters (e.g. the Average Reference and the Surface Laplacian) are stationary. Stationary filters are conceptually simple, easy to use and fast to compute, but all assume that the EEG signal does not change across sensors and time. Given that ERPs are intrinsically non-stationary, applying stationary filters can lead to misinterpretations of the measured neural activity. In contrast, adaptive spatial filters (e.g. Independent Component Analysis, ICA; and Principal Component Analysis, PCA) infer their weights directly from the spatial properties of the data. They are thus not affected by the shortcomings of stationary filters. The issue with adaptive filters is that understanding how they work and how to interpret their output requires advanced statistical and physiological knowledge. Here we describe a novel, easy-to-use and conceptually-simple adaptive filter (Local Spatial Analysis, LSA) for highlighting local components masked by large widespread activity. This approach exploits the statistical information stored in the trial-by-trial variability of stimulus-evoked neural activity to estimate the spatial filter parameters adaptively at each time point. Using both simulated data and real ERPs elicited by stimuli of four different sensory modalities (audition, vision, touch, pain), we show that this method outperforms widely-used stationary filters and allows identifying novel ERP components masked by large widespread activity. Implementation of the LSA filter in Matlab is freely available to download.

Offset analgesia (OA) studies have found that small decreases in the intensity of a tonic noxious heat stimulus yield a disproportionately large amount of pain relief. In the classic OA paradigm, the decrease in stimulus intensity is preceded by an increase of equal size from an initial noxious level. Although the majority of researchers believe this temporal sequence of two changes is important for eliciting OA, it has also been suggested that the temporal contrast mechanism underlying OA may enhance detection of simple, isolated decreases in noxious heat. To test whether decreases in noxious heat intensity, by themselves, are perceived better than increases of comparable sizes, we used an adaptive two-interval alternative forced choice task to find perceptual thresholds for increases and decreases in radiant and contact heat. Decreases in noxious heat were more difficult to perceive than increases of comparable sizes from the same initial temperature of 45°C. In contrast, decreases and increases were perceived equally well within a common range of noxious temperatures (i.e., when increases started from 45°C and decreases started from 47°C). In another task, participants rated the pain intensity of heat stimuli that randomly and unpredictably increased, decreased, or remained constant. Ratings of unpredictable stimulus decreases also showed no evidence of perceptual enhancement. Our results demonstrate that there is no temporal contrast enhancement of simple, isolated decreases in noxious heat intensity. Combined with previous OA findings, they suggest that long-lasting noxious stimuli that follow an increase-decrease pattern may be important for eliciting the OA effect. NEW & NOTEWORTHY Previous research suggested that a small decrease in noxious heat intensity feels surprisingly large because of sensory enhancement of noxious stimulus offsets (a simplified form of “offset analgesia”). Using a two-alternative forced choice task where participants detected simple increases or decreases in noxious heat, we showed that decreases in noxious heat, by themselves, are no better perceived than increases of comparable sizes. This suggests that a decrease alone is not sufficient to elicit offset analgesia.

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