Using Muse: Rapid Mobile Assessment of Brain Performance.

The advent of mobile electroencephalography (mEEG) has created a means for large scale collection of neural data thus affording a deeper insight into cognitive phenomena such as cognitive fatigue. Cognitive fatigue - a neural state that is associated with an increased incidence of errorful performance - is responsible for accidents on a daily basis which at times can cost human lives. To gain better insight into the neural signature of cognitive fatigue in the present study we used mEEG to examine the relationship between perceived cognitive fatigue and human-event related brain potentials (ERPs) and electroencephalographic (EEG) oscillations in a sample of 1,000 people. As a secondary goal, we wanted to further demonstrate the capability of mEEG to accurately measure ERP and EEG data. To accomplish these goals, participants performed a standard visual oddball task on an Apple iPad while EEG data were recorded from a Muse EEG headband. Counter to traditional EEG studies, experimental setup and data collection was completed in less than seven minutes on average. An analysis of our EEG data revealed robust N200 and P300 ERP components and neural oscillations in the delta, theta, alpha, and beta bands. In line with previous findings we observed correlations between ERP components and EEG power and perceived cognitive fatigue. Further, we demonstrate here that a linear combination of ERP and EEG features is a significantly better predictor of perceived cognitive fatigue than any ERP or EEG feature on its own. In sum, our results provide validation of mEEG as a viable tool for research and provide further insight into the impact of cognitive fatigue on the human brain.

When theory and biology differ: The relationship between reward prediction errors and expectancy.

Comparisons between expectations and outcomes are critical for learning. Termed prediction errors, the violations of expectancy that occur when outcomes differ from expectations are used to modify value and shape behaviour. In the present study, we examined how a wide range of expectancy violations impacted neural signals associated with feedback processing. Participants performed a time estimation task in which they had to guess the duration of one second while their electroencephalogram was recorded. In a key manipulation, we varied task difficulty across the experiment to create a range of different feedback expectancies - reward feedback was either very expected, expected, 50/50, unexpected, or very unexpected. As predicted, the amplitude of the reward positivity, a component of the human event-related brain potential associated with feedback processing, scaled inversely with expectancy (e.g., unexpected feedback yielded a larger reward positivity than expected feedback). Interestingly, the scaling of the reward positivity to outcome expectancy was not linear as would be predicted by some theoretical models. Specifically, we found that the amplitude of the reward positivity was about equivalent for very expected and expected feedback, and for very unexpected and unexpected feedback. As such, our results demonstrate a sigmoidal relationship between reward expectancy and the amplitude of the reward positivity, with interesting implications for theories of reinforcement learning.

Older adults display diminished error processing and response in a continuous tracking task.

Advancing age is often accompanied by a decline in motor control that results in a decreased ability to successfully perform motor tasks. While there are multiple factors that contribute to age-related deficits in motor control, one unexplored possibility is that age-related deficits in our ability to evaluate motor output result in an increase in motor errors. In line with this, previous work from our laboratory demonstrated that motor errors evoked an error-related negativity (ERN)-a component of the human ERP associated with error evaluation originating within the human medial-frontal cortex. In the present study, we examined whether or not deficits in the medial-frontal error evaluation system contribute to age-related deficits in motor control. Two groups of participants (young, old) performed a computer-based tracking task that paralleled driving while EEG data were recorded. Our results show that older adults committed more behavioral errors than young adults during performance of the tracking task. An analysis of our ERP data revealed that the amplitude of the ERN was reduced in older adults relative to young adults following motor errors. Our results make an important extension from previous work demonstrating age-related reductions in the ERN during performance of cognitive tasks. Importantly, our results imply the possibility of understanding motor deficits in older age.