Binary Models for Motor-Imagery Brain–Computer Interfaces: Sparse Random Projection and Binarized SVM
- Conference Paper
Rights / licenseIn Copyright - Non-Commercial Use Permitted
Successful motor imagery brain–computer (MI-BCI) 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. Show more
Book title2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)
Pages / Article No.
SubjectEEG; Motor imagery; Embedding; Sparse random projection; Binarized SVM; Hamming distance
Organisational unit03996 - Benini, Luca / Benini, Luca
780215 - Computation-in-memory architecture based on resistive devices (EC)
NotesConference postponed due to Corona virus (COVID-19). Due to the Corona virus (COVID-19) the conference was conducted virtually.
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