Abstract Time Series Classification (TSC) involves building predictive models for a discrete
target variable from ordered, real valued, attributes. Over recent years, a new set of TSC …

Brain–computer interfaces (BCIs) development is closely related to physics. In this paper, we
review the physical principles of BCIs, and underlying novel approaches for registration …

In 2002, the UCR time series classification archive was first released with sixteen datasets. It
gradually expanded, until 2015 when it increased in size from 45 datasets to 85 datasets. In …

Brain computer interfaces (BCI) for the rehabilitation of motor impairments exploit
sensorimotor rhythms (SMR) in the electroencephalogram (EEG). However, the …

The increase in spatiotemporal resolution of neuroimaging devices is accompanied by a
trend towards more powerful multivariate analysis methods. Often it is desired to interpret the …

The performance of brain-computer interfaces (BCIs) improves with the amount of available
training data; the statistical distribution of this data, however, varies across subjects as well …

A brain-computer interface (BCI) is a hardware and software communications system that
permits cerebral activity alone to control computers or external devices. The immediate goal …

An up-to-date account of the interplay between optimization and machine learning,
accessible to students and researchers in both communities. The interplay between …

Analyzing brain states that correspond to event related potentials (ERPs) on a single trial
basis is a hard problem due to the high trial-to-trial variability and the unfavorable ratio …

Brain–Computer Interfaces Handbook: Technological and Theoretical Advances provides a
tutorial and an overview of the rich and multi-faceted world of Brain–Computer Interfaces …