Sequential Bayesian Learning of Biosignal Patterns

Biosignals have non-stationary properties that change over time, so conventional static analysis methods have limitations. In this research, we are developing learning algorithms that can adapt to changing biosignal patterns by using a sequential Bayesian learning framework to recursively update the posterior distribution of classification model parameters. In particular, by constructing Bayesian classification models based on stochastic generative models of EMG signals and EEG, and connecting them to sequential Bayesian updates, we realize more reliable sequential learning that appropriately considers the uncertainty of biosignals. We have demonstrated the effectiveness through applications such as within-subject continual learning and between-subject transfer learning of EMG signals.