ABSTRACT
Reference frame transformations are usually considered to be deterministic. However, translations, scaling or rotation angles could be stochastic. Indeed, variability of these entities often originates from noisy estimation processes. The impact of transformation noise on the statistics of the transformed signals is unknown and a quantification of these effects is the goal of this study. We first quantify analytically and numerically how stochastic reference frame transformations (SRFT) alter the posterior distribution of the transformed signals. We then propose an new empirical measure to quantify deviations from a given distribution when only limited data is available. We apply this empirical measure to an example in sensory-motor neuroscience to quantify how different head roll angles change the distribution of reach endpoints away from the normal distribution.
ABSTRACT
In this paper we propose an agglomerative hierarchical clustering Ward's algorithm in tandem with the Affinity Propagation algorithm to reliably localize active brain regions from magnetoencephalography (MEG) brain signals. Reliable localization of brain areas with MEG has been difficult due to variations in signal strength, and the spatial extent of the reconstructed activity. The proposed approach to resolve this difficulty is based on adaptive clustering on reconstructed beamformer images to find locations that are consistently active across different participants and experimental conditions with high spatial resolution. Using data from a human reaching task, we show that the method allows more accurate and reliable localization from MEG data alone without using functional magnetic resonance imaging (fMRI) or any other imaging techniques.
