On May 6, 2022, Prof. Kesh Kesavadas (University at Albany) gave a free online presentation “Brain Computer Interface and Control of Manufacturing Robots,” based on his recent JCISE paper (April 2022 issue), co-authored with Yao Li (University of Illinois Urbana-Champaign). To view the free webinar please click the button link below.
Abstract: As more industrial robots share a workspace with humans, the need for closer communications becomes a priority. Unfortunately, the current generation of robot interfaces are not designed to understand human intentions. To overcome this, we have developed human-robot interactions based on Brain-Computer Interfaces (BCIs) capable of talking to robots directly. By collecting and encoding brain activities with BCIs, humans can actively send commands to robots or passively monitor mental activities. We demonstrate this concept through two manufacturing applications: BCI-based robot control for welding and BCI integrated system to pick defective parts from a conveyor. To improve communication, we have developed several new techniques and algorithms. One, in particular, the Conv-CA model, which combines convolutional neural network (CNN) and canonical correlation analysis (CCA), has been shown to improve the performance of the state-of-art steady-state visually evoked potential (SSVEP) technique. Further, we have also been studying passive BCI communications, where robot monitors human brain activity for the fear response. This technique can make robots more friendly in environments where close interaction occurs between man and machine.
Citation: Li, Y., and Kesavadas, T. (July 21, 2021). “SSVEP-Based Brain-Computer Interface for Part-Picking Robotic Co-Worker.” ASME. J. Comput. Inf. Sci. Eng. April 2022; 22(2): 021001. https://doi.org/10.1115/1.4051596
