Collaborative Brain-Computer Interface for Human Interest Detection in Complex and Dynamic Settings

Humans can fluidly adapt their interest in complex environments in ways that machines cannot. Here, we lay the groundwork for a real-world system that passively monitors and merges neural correlates of visual interest across team members via Collaborative Brain Computer Interface (cBCI). When group interest is detected and co-registered in time and space, it can be used to model the task relevance of items in a dynamic, natural environment. Previous work in cBCIs focuses on static stimuli, stimulus- or response- locked analyses, and often within-subject and experiment model training. The contributions of this work are twofold. First, we test the utility of cBCI on a scenario that more closely resembles natural conditions, where subjects visually scanned a video for target items in a virtual environment. Second, we use an experiment-agnostic deep learning model to account for the real-world use case where no training set exists that exactly matches the end-users task and circumstances. With our approach we show improved performance as the number of subjects in the cBCI ensemble grows, and the potential to reconstruct ground-truth target occurrence in an otherwise noisy and complex environment.Comment: 6 pages, 6 figure

A machine learning approach to taking EEG-based brain-computer interfaces out of the lab

Despite being a subject of study for almost three decades, non-invasive brain- computer interfaces (BCIs) are still trapped in the laboratory. In order to move into more common use, it is necessary to have systems that can be reliably used over time with a minimum of retraining. My research focuses on machine learning methods to minimize necessary retraining, as well as a data science approach to validate processing pipelines more robustly. Via a probabilistic transfer learning method that scales well to large amounts of data in high dimensions it is possible to reduce the amount of calibration data needed for optimal performance. However, a good model still requires reliable features that are resistant to recording artifacts. To this end we have also investigated a novel feature of the electroencephalogram which is predictive of multiple types of brain-related activity. As cognitive neuroscience literature suggests, shifts in the peak frequency of a neural oscillation – hereafter referred to as frequency modulation – can be predictive of activity in standard BCI tasks, which we validate for the first time in multiple paradigms. Finally, in order to test the robustness of our techniques, we have built a codebase for reliable comparison of pipelines across over fifteen open access EEG datasets