Deep Learning for Real-Time Neural Decoding of Grasp

Deep Learning for Real-Time Neural Decoding of Grasp. Viviani, P., Gesmundo, I., Ghinato, E., Agudelo-Toro, A., Vercellino, C., Vitali, G., Bergamasco, L., Scionti, A., Ghislieri, M., Agostini, V., Terzo, O., & Scherberger, H. In De Francisci Morales, G., Perlich, C., Ruchansky, N., Kourtellis, N., Baralis, E., & Bonchi, F., editors, Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track, pages 379–393, Cham, 2023. Springer Nature Switzerland.
doi abstract bibtex

Neural decoding involves correlating signals acquired from the brain to variables in the physical world like limb movement or robot control in Brain Machine Interfaces. In this context, this work starts from a specific pre-existing dataset of neural recordings from monkey motor cortex and presents a Deep Learning-based approach to the decoding of neural signals for grasp type classification. Specifically, we propose here an approach that exploits LSTM networks to classify time series containing neural data (i.e., spike trains) into classes representing the object being grasped.

@InProceedings{10.1007/978-3-031-43427-3_23,
  author="Viviani, Paolo
  and Gesmundo, Ilaria
  and Ghinato, Elios
  and Agudelo-Toro, Andres
  and Vercellino, Chiara
  and Vitali, Giacomo
  and Bergamasco, Letizia
  and Scionti, Alberto
  and Ghislieri, Marco
  and Agostini, Valentina
  and Terzo, Olivier
  and Scherberger, Hansj{\"o}rg",
  editor="De Francisci Morales, Gianmarco
  and Perlich, Claudia
  and Ruchansky, Natali
  and Kourtellis, Nicolas
  and Baralis, Elena
  and Bonchi, Francesco",
  title="Deep Learning for Real-Time Neural Decoding of Grasp",
  booktitle="Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track",
  year="2023",
  publisher="Springer Nature Switzerland",
  address="Cham",
  pages="379--393",
  abstract="Neural decoding involves correlating signals acquired from the brain to variables in the physical world like limb movement or robot control in Brain Machine Interfaces. In this context, this work starts from a specific pre-existing dataset of neural recordings from monkey motor cortex and presents a Deep Learning-based approach to the decoding of neural signals for grasp type classification. Specifically, we propose here an approach that exploits LSTM networks to classify time series containing neural data (i.e., spike trains) into classes representing the object being grasped.",
  isbn="978-3-031-43427-3",
doi = {10.1007/978-3-031-43427-3_23}
}

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