Successful motor imagery brain-computer (MIBCI) algorithms typically rely on a large number of features used in a classifier with real-valued weights that render them unsuitable for real-time execution on a resource-limited device. We propose a new method that randomly projects a large number of real-valued Riemannian covariance features to a binary space, where a linear SVM classifier can be learned with binary weights too. Flexibly increasing the dimension of binary embedding achieves almost the same accuracy (≤1.27% lower) compared to all models with float16 in 4-class and 3-class MI, yet delivering a more compact model with simpler operations to execute.
Binary Models for Motor-Imagery Brain-Computer Interfaces: Sparse Random Projection and Binarized SVM / Hersche M.; Benini L.; Rahimi A.. - ELETTRONICO. - (2020), pp. 9073968.163-9073968.167. (Intervento presentato al convegno 2020 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2020 tenutosi a ita nel 2020) [10.1109/AICAS48895.2020.9073968].
Binary Models for Motor-Imagery Brain-Computer Interfaces: Sparse Random Projection and Binarized SVM
Benini L.;
2020
Abstract
Successful motor imagery brain-computer (MIBCI) algorithms typically rely on a large number of features used in a classifier with real-valued weights that render them unsuitable for real-time execution on a resource-limited device. We propose a new method that randomly projects a large number of real-valued Riemannian covariance features to a binary space, where a linear SVM classifier can be learned with binary weights too. Flexibly increasing the dimension of binary embedding achieves almost the same accuracy (≤1.27% lower) compared to all models with float16 in 4-class and 3-class MI, yet delivering a more compact model with simpler operations to execute.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
