DETECT: Detection of Events in Continuous Time Toolbox: user's guide, examples, and function reference documentation / Vernon Lawhern and Kay Robbins, W. David Hairston.

Format:

Book

Government document

Author/Creator:

Lawhern, Vernon, author.

Robbins, Kay, author.

Hairston, W. David, author.

Contributor:

U.S. Army Research Laboratory, issuing body.

Series:

ARL-SR ; 269.

ARL-SR ; 269

Language:

English

Subjects (All):

United States. Army.

United States.

MATLAB.

Signal detection--Handbooks, manuals, etc.

Signal detection.

Electroencephalography--Handbooks, manuals, etc.

Electroencephalography.

Genre:

Text

handbooks.

Handbooks and manuals

Handbooks and manuals.

Physical Description:

1 online resource (iv, 50 pages) : illustrations.

Place of Publication:

Aberdeen Proving Ground, MD : Army Research Laboratory, June 2013.

Summary:

DETECT (Detection of Events in Continuous Time) is a MATLAB toolbox for automated event detection in long, continuous multichannel time series. Although developed for electroencephalography (EEG), it uses a universal format that is applicable to many types of physiological time-series data or case uses benefitting from rapid, automated discrimination of specific predefined signals, and is free-standing (requiring no other plugins or packages). The primary goal is a toolbox that is simple for researchers to use, allowing them to quickly train a model on multiple classes of events, assess the accuracy of the model, and determine how closely the results agree with their own manual identification of events without requiring extensive programming knowledge or machine learning experience. Here, we provide reference documentation covering use of the DETECT toolbox, including an overview, explanations of each of the primary components and how they interact, and full help documentation for each function in the toolbox. Additionally we provide six example uses of the toolbox, including labeling trials, labeling continuous time series, manually labeling data, plotting labeled data, updating previously labeled dataset, and comparing two labeled datasets.

The original document contains color images. Prepared in collaboration with University of Texas, Dept. of Computer Science, San Antonio, TX.

Notes:

"June 2013."

Approved for public release; distribution is unlimited.

Description based on online resource; title from PDF title page (DTIC website, viewed April 22, 2020).

OCLC:

872740347

Access Restriction:

Open access content Open access content