  Accurate, fast, and reliable multiclass classification of
electroencephalography (EEG) signals is a challenging task towards the
development of motor imagery brain-computer interface (MI-BCI) systems. We
propose enhancements to different feature extractors, along with a support
vector machine (SVM) classifier, to simultaneously improve classification
accuracy and execution time during training and testing. We focus on the
well-known common spatial pattern (CSP) and Riemannian covariance methods, and
significantly extend these two feature extractors to multiscale temporal and
spectral cases. The multiscale CSP features achieve 73.70$\pm$15.90% (mean$\pm$
standard deviation across 9 subjects) classification accuracy that surpasses
the state-of-the-art method [1], 70.6$\pm$14.70%, on the 4-class BCI
competition IV-2a dataset. The Riemannian covariance features outperform the
CSP by achieving 74.27$\pm$15.5% accuracy and executing 9x faster in training
and 4x faster in testing. Using more temporal windows for Riemannian features
results in 75.47$\pm$12.8% accuracy with 1.6x faster testing than CSP.
