Data from: Classifying three imaginary states of the same upper extremity using time-domain features

Brain-computer interface (BCI) allows collaboration between humans and
machines. It translates the electrical activity of the brain to
understandable commands to operate a machine or a device. In this study,
we propose a method to improve the accuracy of a 3-class BCI using
electroencephalographic (EEG) signals. This BCI discriminates rest against
imaginary grasps and elbow movements of the same limb. This classification
task is challenging because imaginary movements within the same limb have
close spatial representations on the motor cortex area. The proposed
method extracts time-domain features and classifies them using a support
vector machine (SVM) with a radial basis kernel function (RBF). An average
accuracy of 74.2% was obtained when using the proposed method on a dataset
collected, prior to this study, from 12 healthy individuals. This accuracy
was higher than that obtained when other widely used methods, such as
common spatial patterns (CSP), filter bank CSP (FBCSP), and band power
methods, were used on the same dataset. These results are encouraging and
the proposed method could potentially be used in future applications
including BCI-driven robotic devices, such as a portable exoskeleton for
the arm, to assist individuals with impaired upper extremity functions in
performing daily tasks.