摘要：A novel 4-class single-trial brain computer interface (BCI) based
on two (rather than four or more) binary linear discriminant analysis
(LDA) classifiers is proposed, which is called a “parallel BCI.” Unlike
other BCIs where mental tasks are executed and classified in a serial
way one after another, the parallel BCI uses properly designed parallel
mental tasks that are executed on both sides of the subject body
simultaneously, which is the main novelty of the BCI paradigm used
in our experiments. Each of the two binary classifiers only classifies
the mental tasks executed on one side of the subject body, and the
results of the two binary classifiers are combined to give the result
of the 4-class BCI. Data was recorded in experiments with both real
movement and motor imagery in 3 able-bodied subjects. Artifacts
were not detected or removed. Offline analysis has shown that, in
some subjects, the parallel BCI can generate a higher accuracy than a
conventional 4-class BCI, although both of them have used the same
feature selection and classification algorithms.
