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{viviani_ecml_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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