Authors:

Albert Li

Date Created:

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About Paper:

Simultaneous demand for synchronized cognitive and motor activity makes learning to operate multirotor systems, such as drones, tedious and challenging for humans. To address this, the objective of this research is to optimize the learning process for novice drone pilots by developing a physical brain-computer interface that dynamically increases the difficulty of a simulated drone landing task based on participants' cognitive workload in real time. This system utilizes functional near-infrared spectroscopy (fNIRS), a noninvasive neuroimaging technique that measures cognitive load through cortical hemodynamic activity. As participants become more proficient at the task, our system gradually removes computer-assisted controls corresponding to the user's decrease in cognitive workload. This process incrementally increases the difficulty of the task, with the end goal of achieving full manual control by the user and successful performance of the simulated landing task within an hour-long training session. Future pilot studies will be conducted to evaluate the effectiveness of cognitive load measurements as a psychophysiological method to enhance motor learning for novice drone pilots. The performance of novice participants using our system will be compared against those learning to perform the task completely manually. Ultimately, we aim to translate this technology to real drones, enhancing training protocols and operational efficiency for novice pilots in real-world scenarios. Keywords: Brain-Computer Interface; Functional Near-Infrared Spectroscopy; Cognitive Learning Theory

Source:

Purdue University / 2024

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No topics listed

Co-authors:

Albert Li