  Brain-computer interface (BCI) aims to establish and improve human and
computer interactions. There has been an increasing interest in designing new
hardware devices to facilitate the collection of brain signals through various
technologies, such as wet and dry electroencephalogram (EEG) and functional
near-infrared spectroscopy (fNIRS) devices. The promising results of machine
learning methods have attracted researchers to apply these methods to their
data. However, some methods can be overlooked simply due to their inferior
performance against a particular dataset. This paper shows how relatively
simple yet powerful feature selection/ranking methods can be applied to speech
imagery datasets and generate significant results. To do so, we introduce two
approaches, horizontal and vertical settings, to use any feature selection and
ranking methods to speech imagery BCI datasets. Our primary goal is to improve
the resulting classification accuracies from support vector machines,
$k$-nearest neighbour, decision tree, linear discriminant analysis and long
short-term memory recurrent neural network classifiers. Our experimental
results show that using a small subset of channels, we can retain and, in most
cases, improve the resulting classification accuracies regardless of the
classifier.
