NeuroTeaming: Using Power Spectral Density for Adjusting Teaming Dynamics in Pilot-AI Task Allocation

Paul, Tanya, Lafond, Daniel and Marois, Alexandre orcid iconORCID: 0000-0002-4127-4134 (2024) NeuroTeaming: Using Power Spectral Density for Adjusting Teaming Dynamics in Pilot-AI Task Allocation. In: Neuroergonomics and Cognitive Engineering. AHFE International, pp. 23-33. ISBN 978-1-964867-02-1

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Human-autonomy teaming (HAT) is becoming a subject of high interest in the human factors literature. It has several applications, including the collaboration between a human and an autonomous unmanned aerial vehicle (UAV) for security and defence use cases (e.g., for search and rescue tasks). This work is focused on methods for task-allocation between human and autonomous UAV agents. The proposed approach is human-centred, using a coactive design framework which relies on enabling adaptive team dynamics where different agents might act as key players for specific tasks based on an interdependent relationship. This method helps solve complex issues in understanding and adjusting to complementary team dynamics where agents might have different skill levels, experiences, roles, and helps understand which agent is more competent to perform a task. Additionally, such a framework promotes transparency towards the control and task-allocation strategies. To demonstrate this task-allocation strategy, this study looked at the use of neurophysiological features as indicators of task-specific capacities in UAV operations, more specifically electroencephalogram (EEG) signals, which opens up for the development of task-allocation adaptive systems, dependent upon variations in brain activity. Results found that EEG spectral power bands have potential to help determine different task-based abilities across groups (i.e., obstacle avoidance vs. target identification), hence contributing to pinpointing variations in the type of autonomous support needed. Overall, this research explores how task-dependencies can be observed through EEG signals for better transparency and explainability of adaptive control in pilot-AI teaming.

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