Tag Archives: Research

Paper Accepted at SMC2020

Finally a good news in these difficult times!
I’m proud to announce that our paper “ROS-Neuro Integration of Deep Convolutional Autoencoders for EEG Signal Compression in Real-time BCIs” has been accepted at the 2020 IEEE International Conference on Systems, Man, and Cybernetics. I thank my co-authors for their work and support.

In the paper, we use a convolutional autoencoder to compress EEG signals, and deploy the trained model inside a ROS-Neuro node, in order to allow for an efficient real-time processing of the input data. This can be a first step towards many interesting BCI applications, such as the remote control of machine, with only the power of tought! So cool!

Update: the paper is now avaialable on the arXiv here: arXiv:2008.13485

MusAE paper accepted at ECAI2020

Great news!
My paper on MusAE, a generative model for music editing and generation, has been officially accepted at the 24th European Conference on Artificial Intelligence. I thank my co-authors Antonio Carta and Davide Bacciu for their work and support. I look forward to present this work next June in the suggestive location of Santiago de Compostela.

Here you can find a link to some additional material, comprising of generated songs and interpolations. You can also find the full paper on ArXiv.