To date, the vast majority of what we know about brain activity, function, and the links between physiology and behavior has been derived from laboratory and medical domains. While the practices of controlling environmental variables and limiting outside sources of noise and variance have been critical for developing interpretable models of brain activity, they have also left us without a good appreciation of how neural dynamics occur “in the wild.” That is, despite a rapidly increasing interest within scientific and larger every-day user communities to integrate neuroimaging into daily life, we do not have a good handle on how much of our understanding from laboratory neuroscience translates into prediction of real-world behaviors. The real-world neuroimaging project is specifically intended to address this deficit. The long-term goals of this work is to improve our understanding of the neural dynamics that underpin functional behaviors in realistic, real-world scenarios and develop a set of tools to acquire, analyse, interpret and predict real-time, real-world brain activity in order to predict and mediate dynamic human physiology-behavior relationships, and facilitate the design and performance of novel human-computer interactive systems (McDowell et al, 2013).
Our work within this project falls under two primary domains (1) development of novel technologies and approaches to enable real-world neuroimaging, and (2) developing hypotheses and theory through experimentation focused on enhancing our understanding of neural dynamics in real-world conditions.
Real-world Neuroimaging Technologies
Work in this area aims to advance the state of the art for technologies and techniques for assessment of brain activity as it occurs within real-world settings, where neuroimaging conditions are less than ideal and target cognitive states are often elusive or even vastly different than those found in traditional laboratory scenarios. The focus is primarily on developing novel technologies and techniques that will enable or enhance experimentation or the implementation of human-computer interactive systems. Example topic areas include:
Development and testing of novel neuroimaging hardware, such as: dry, non-metallic, highly flexible sensors for EEG that are comfortable yet conductively stable under compression; ultra-low-power and low-user impact system design, allowing user transparency; and methods for rapid-prototyping of user-specific cap design
Development and use of novel methods and tools for assessing data quality, such as designing and constructing EEG “phantom” testbed devices, new analytical comparison techniques, approaches to tease apart sources of non-brain artifacts in real-world data, and establishing community-accepted benchmarks for signal reliability and validity.
Novel software algorithms to account for and mitigate non-brain artifacts, improving data signal-to-noise ratio (SNR), improving interpretation of noisy, non-stationary data, and utilizing multi-sensor, multi-modality data streams in and efficient and cohesive manner.
Paradigms for Real-world Neuroimaging As a broad community of scientists, we have established a rudimentary understanding of brain processes in isolated tasks and environments. However, we have little grasp on how laboratory-based knowledge scales with task and environmental complexity. The nervous system is among the most complex and dynamic systems in the body and, despite impressions given by advanced imaging technologies as applied in laboratory studies and medical preparations, it is unlikely that the cognitive, affective, and motor processes that support performance are dissociable enough to be understood in isolation from one another. This presents a fundamental challenge for extending laboratory-derived theories and human experimentation research to real world conditions. The goal of the Real-World Neuroimaging Project is to identify principled ways to translate observations, findings, and theory from conventional neuroscience into real-world, every-day scenarios.
Towards this end, efforts in this area fall within three primary domains: (1) Developing novel, creative paradigms for imaging and interpreting neural dynamics as they support performance of real-world tasks, (2) expanding paradigms and models to account for the influence of real-life external factors, such as stress, fatigue, personality, and sociocultural context on neuro-behavioral dynamics, and (3) developing theoretical approaches that enable the observation and interpretation of neurophysiological activity in non-traditional, highly realistic tasks and scenarios.
Example topics areas include:
Understanding cortical and behavioral dynamics during real on-road driving, and leveraging highly multi-modal data in classification approaches to enable prediction of critical decisions during performance in simulated and real-world driving tasks;
Characterizing interactions between interpersonal social dynamics, individual driving behavior, and individual preferences and proclivities within driver-passenger dyads;
Cognitive computations underlying visual search with high dynamic range luminance;
Leveraging saccade and fixation related potentials (S/FRPs) to investigate perceptual and cognitive processes absent in traditional fixation-constrained paradigms enabling a more holistic understanding of visual search in real-world scenarios. Specific areas include: retinal and extra-retinal influences on early visual perception, endogenous and exogenous pre-saccadic shifts of attention, bottom-up (salience) and top-down (arousal, cognitive load) effects on eye movements and FRPs during visual search.