Active Inference as a Framework for Brain-Computer Interfaces

As Karl Friston explained during the International Symposium on Artificial Intelligence and Brain Science 2020, active inference provides a way of using abstract rule-learning and approximate Bayesian inference to show how minimizing (expected) free energy leads to active sampling of novel contingencies. Friston elaborated how there were ways of making an optimal decision using active inference that can offer perspectives to advances in artificial intelligence. These methods of optimization within the context of active inference can also be used as a framework for improving brain-computer interfaces (BCI). This way, BCIs can give rise to artificial curiosity in the way Friston had described during his session. Using Friston’s free energy principle, we can optimize the criterion a BCI uses to infer the intentions of the user from EEG observations. Under Friston’s criteria for making an optimal decision, BCIs can expand their framework of optimal decision-making using active inference.