A POMDP-Based Optimal Control of P300-Based Brain-Computer Interfaces

Authors

  • Jaeyoung Park Korea Advanced Institute of Science and Technology
  • Kee-Eung Kim Korea Advanced Institute of Science and Technology
  • Yoon-Kyu Song Seoul National University

Abstract

Most of the previous work on brain-computer interfaces (BCIs) exploiting the P300 in electroencephalography (EEG) has focused on low-level signal processing algorithms such as feature extraction and classification methods. Although a significant improvement has been made in the past, the accuracy of detecting P300 is limited by the inherently low signal-to-noise ratio in EEGs. In this paper, we present a systematic approach to optimize the interface using partially observable Markov decision processes (POMDPs). Through experiments involving human subjects, we show the P300 speller system that is optimized using the POMDP achieves a significant performance improvement in terms of the communication bandwidth in the interaction.

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Published

2011-08-04

How to Cite

Park, J., Kim, K.-E., & Song, Y.-K. (2011). A POMDP-Based Optimal Control of P300-Based Brain-Computer Interfaces. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 1559-1562. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/7956

Issue

Section

New Scientific and Technical Advances in Research