Abstract
A great challenge for brain-computer interface (BCI) systems is their deployment in clinical settings or at home, where a BCI system can be used with limited calibration sessions. BCI should be ideally self-trained and take advantage of unlabeled data. When performing a task, the EEG signals change over time, hence the recorded signals have non-stationary properties. It is necessary to provide machine-learning approaches that can deal with self-training and/or use semi-supervised learning methods for signal classification. A key problem in graph-based semi-supervised learning is determining the characteristics of the affinity matrix that defines the relationships between examples, including the size of the neighborhood of each example. In this paper, we propose two approaches for building the affinity matrix using the distance between examples and the number of neighbors, with a limited number of hyper-parameters, making it easy to reuse. We also compare the Euclidean distance and Riemannian geometry distances to construct the affinity matrix. We assess the classification performance with motor imagery data with two classes from a publicly available dataset of 14 participants. The results show the interest of the proposed semi-supervised approaches with the use of distances to define the neighborhood using Riemannian geometry-based distances with an average accuracy of 73.75%.
This study was supported by the NIH-R15 NS118581 project.
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Smrkovsky, E., Cecotti, H. (2023). Graph-Based Semi-supervised Learning Using Riemannian Geometry Distance for Motor Imagery Classification. In: Rodríguez-González, A.Y., Pérez-Espinosa, H., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2023. Lecture Notes in Computer Science, vol 13902. Springer, Cham. https://doi.org/10.1007/978-3-031-33783-3_30
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DOI: https://doi.org/10.1007/978-3-031-33783-3_30
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