Reducing multi-sensor data to a single time course that reveals experimental effects
1 INSERM, Cognitive Neuroimaging Unit, Gif sur Yvette 91191, France
2 Commissariat à l’Energie Atomique, Direction des Sciences du Vivant, I2BM, NeuroSpin center, Gif sur Yvette 91191, France
3 Université Paris-Sud 11, Orsay 91405, France
4 Collège de France, 11 Place Marcelin Berthelot, Paris 75005, France
Citation and License
BMC Neuroscience 2013, 14:122 doi:10.1186/1471-2202-14-122Published: 14 October 2013
Multi-sensor technologies such as EEG, MEG, and ECoG result in high-dimensional data sets. Given the high temporal resolution of such techniques, scientific questions very often focus on the time-course of an experimental effect. In many studies, researchers focus on a single sensor or the average over a subset of sensors covering a “region of interest” (ROI). However, single-sensor or ROI analyses ignore the fact that the spatial focus of activity is constantly changing, and fail to make full use of the information distributed over the sensor array.
We describe a technique that exploits the optimality and simplicity of matched spatial filters in order to reduce experimental effects in multivariate time series data to a single time course. Each (multi-sensor) time sample of each trial is replaced with its projection onto a spatial filter that is matched to an observed experimental effect, estimated from the remaining trials (Effect-Matched Spatial filtering, or EMS filtering). The resulting set of time courses (one per trial) can be used to reveal the temporal evolution of an experimental effect, which distinguishes this approach from techniques that reveal the temporal evolution of an anatomical source or region of interest.
We illustrate the technique with data from a dual-task experiment and use it to track the temporal evolution of brain activity during the psychological refractory period. We demonstrate its effectiveness in separating the means of two experimental conditions, and in significantly improving the signal-to-noise ratio at the single-trial level. It is fast to compute and results in readily-interpretable time courses and topographies. The technique can be applied to any data-analysis question that can be posed independently at each sensor, and we provide one example, using linear regression, that highlights the versatility of the technique.
The approach described here combines established techniques in a way that strikes a balance between power, simplicity, speed of processing, and interpretability. We have used it to provide a direct view of parallel and serial processes in the human brain that previously could only be measured indirectly. An implementation of the technique in MatLab is freely available via the internet.