Bayesian learning from multi-way EEG feedback for robot navigation and target identification

Journal article

Many brain-computer interfaces require a high mental workload. Recent research has shown that this could be greatly alleviated through machine learning, inferring user intentions via reactive brain responses. These signals are generated spontaneously while users merely observe assistive robots performing tasks. Using reactive brain signals, existing studies have addressed robot navigation tasks with a very limited number of potential target locations. Moreover, they use only binary, error-vs-correct classification of robot actions, leaving more detailed information unutilised. In this study a virtual robot had to navigate towards, and identify, target locations in both small and large grids, wherein any location could be the target. For the first time, we apply a system utilising detailed EEG information: 4-way classification of movements is performed, including specific information regarding when the target is reached. Additionally, we classify whether targets are correctly identified. Our proposed Bayesian strategy infers the most likely target location from the brain's responses. The experimental results show that our novel use of detailed information facilitates a more efficient and robust system than the state-of-the-art. Furthermore, unlike state-of-the-art approaches, we show scalability of our proposed approach: By tuning parameters appropriately, our strategy correctly identifies 98% of targets, even in large search spaces.

Authors: Wirth, C; Toth, J; Arvaneh, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: Scientific Reports
• Volume: 13
• Issue: 1
• Pages: 16925-16925
• Publication date: 2023-10-07
• DOI: 10.1038/s41598-023-44077-8
• EISSN: 2045-2322
• ISSN: 2045-2322
• Language: English
• Keywords: Artificial intelligence; Binary classification; Bayesian inference; Computer science; Identification (biology); Brain–computer interface; Support vector machine; Robot; Workload; Human–computer interaction; Electroencephalography; Scalability; Machine learning; Bayesian probability