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.
Hersche M., Benini L., Rahimi A. (2020). Binary Models for Motor-Imagery Brain-Computer Interfaces: Sparse Random Projection and Binarized SVM. Institute of Electrical and Electronics Engineers Inc. [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.
