Tagged: Neuroscience

Advanced Technologies for Brain Research [Scanning the Issue]

We believe that this special issue will serve to increase the public awareness and foster discussions on the multiple worldwide BRAIN initiatives, both within and outside the IEEE, providing an impetus for development of long-term cost-effective healthcare solutions. We also believe that the topics presented in this special issue will serve as scientific evidence for health and policy advocates of the value of neurotechnologies for improving the neurological and mental health and wellbeing of the general population. Below we briefly highlight the papers and technologies in this special issue.

Correlating Speaker Gestures in Political Debates with Audience Engagement Measured via EEG

We hypothesize that certain speaker gestures can convey significant information that are correlated to audience engagement. We propose gesture attributes, derived from speakers’ tracked hand motions to automatically quantify these gestures from video. Then, we demonstrate a correlation between gesture attributes and an objective method of measuring audience engagement: electroencephalography (EEG) in the domain of political debates. We collect 47 minutes of EEG recordings from each of 20 subjects watching clips of the 2012 U.S. Presidential debates. The subjects are examined in aggregate and in subgroups according to gender and political affiliation. We find statistically significant correlations between gesture attributes (particularly extremal pose) and our feature of engagement derived from EEG both with and without audio. For some stratifications, the Spearman rank correlation reaches as high as ρ = 0.283 with p < 0.05, Bonferroni corrected. From these results, we identify those gestures that can be used to measure engagement, principally those that break habitual gestural patterns.

Neuro-Robotic Technologies and Social Interactions

The current bandwidth for understanding cognitive and emotional context of a person is much more limited between robots and humans than among humans. Advances in human sensing technologies over the past two decades hold promise for providing online and unique information sources that can lead to deeper insights into human cognitive and emotional state than are currently attainable. However, blind application of the human sensing technologies alone is not a solution. Here, we focus on the integration of neuroscience with robotic technologies for improving social interactions. We discuss the issue of uncertainty in human state detection and the need to develop approaches to estimate and integrate knowledge of that uncertainty. We illustrate this by discussing two application areas and the potential neuro-robotic technologies that could be developed within them

Perceptual salience as novelty detection in cortical pinwheel space

We describe a filter-based model of orientation processing in primary visual cortex (V1) and demonstrate that novelty in cortical “pinwheel” space can be used as a measure of perceptual salience. In the model, novelty is computed as the negative log likelihood of a pinwheel’s activity relative to the population response. The population response is modeled using a mixture of Gaussians, enabling the representation of complex, multi-modal distributions. Hidden variables that are inferred in the mixture model can be viewed as grouping or “binding” pinwheels which have similar responses within the distribution space. Results are shown for several stimuli that illustrate well-known contextual effects related to perceptual salience, as well as results for a natural image.