SelfRegulationSCP2 {mlmts}

SelfRegulationSCP2

Description

Multivariate time series (MTS) taken from an Amyotrophyc Lateral Sclerosis (ALS) subject asked to move a cursor up and down on a computer screen while his cortical potentials were taken.

Usage

data(SelfRegulationSCP1)

Format

A list with two elements, which are:

data

A list with 380 MTS.

classes

A numeric vector indicating the corresponding classes associated with the elements in data.

Details

Each element in data is a matrix formed by 1152 rows (time points) indicating time recordings over an interval of 4.5 seconds and 7 columns (variables) indicating EEG channel. The first 200 elements correspond to the training set, whereas the last 180 elements correspond to the test set. The numeric vector classes is formed by integers from 1 to 2, indicating that there are 2 different classes in the database. Each class is associated with the label 'negativity' (downward movement of the cursor) or 'positivity' (upward movement of the cursor). For more information, see Bagnall et al. (2018). Run "install.packages("ueadata2", repos="https://anloor7.github.io/drat")" to access this dataset and use the syntax "ueadata2::SelfRegulationSCP2".

References

Bagnall A, Dau HA, Lines J, Flynn M, Large J, Bostrom A, Southam P, Keogh E (2018). “The UEA multivariate time series classification archive, 2018.” arXiv preprint arXiv:1811.00075.

Ruiz AP, Flynn M, Large J, Middlehurst M, Bagnall A (2021). “The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances.” Data Mining and Knowledge Discovery, 35(2), 401–449.

Bagnall A, Lines J, Vickers W, Keogh E (2022). “The UEA & UCR Time Series Classification Repository.” www.timeseriesclassification.com.