What is the simplest model that can account for high-fidelity imitation?

What inductive biases must be incorporated into multi-agent artificial intelligence models to get them to capture high-fidelity imitation? We think very little is needed. In the right environments, both instrumental- and ritual-stance imitation can emerge from generic learning mechanisms operating on non-deliberative decision architectures. In this view, imitation emerges from trial-and-error learning and does not require explicit deliberation.

Recursive channel selection techniques for brain computer interfaces.

Automated channel selection allows the dimension of EEG data to be reduced without expert knowledge. We introduce Recursive Channel Insertion, an extension to Recursive Channel Elimination, which dramatically reduces calculation time with no loss of accuracy. Furthermore we propose Repeated Recursive Channel Insertion, which shows an improvement in accuracy over the previous methods when tested on a standard dataset.

Asynchronous brain computer interface using hidden semi-Markov models.

Ideal Brain Computer Interfaces need to perform asynchronously and at real time. We propose Hidden Semi-Markov Models (HSMM) to better segment and classify EEG data. The proposed HSMM method was tested against a simple windowed method on standard datasets. We found that our HSMM outperformed the simple windowed method. Furthermore, due to the computational demands of the algorithm, we adapted the HSMM algorithm to an online setting and demonstrate that this faster version of the algorithm can run in real time.