Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Time and frequency -Domain Interpretation of PI Control01:27

Time and frequency -Domain Interpretation of PI Control

439
Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires...
439
Time and frequency -Domain Interpretation of Phase-lead Control01:24

Time and frequency -Domain Interpretation of Phase-lead Control

477
Phase-lead controllers are commonly used in various control systems to enhance response speed and stability. Adjusting the brightness on a television screen offers a practical example of phase-lead control. When contrast is enhanced, a phase-lead controller is employed. Mathematically, phase-lead control is identified when the first parameter is smaller than the second.
The design of phase-lead control involves the strategic placement of poles and zeros to balance steady-state error and system...
477
Time and frequency -Domain Interpretation of Phase-lag Control01:21

Time and frequency -Domain Interpretation of Phase-lag Control

423
Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any...
423
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

386
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
386
Frequency-Domain Interpretation of PD Control01:24

Frequency-Domain Interpretation of PD Control

394
Proportional-Derivative (PD) controllers are widely used in fan control systems to improve stability and performance. A fan control system can be effectively represented using a Bode plot to illustrate the impact of a PD controller through its transfer function. The Bode plot visually conveys how PD control modifies the fan's response across various frequencies, providing a frequency domain interpretation of the controller's behavior.
The proportional control gain, combined with the...
394
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

377
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
377

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

What happens when public primary care is ill-prepared to respond to non-communicable diseases: a mixed-method study of diabetes and hypertension care in urban Nepal.

Journal of global health·2026
Same author

An enhanced hypergraph CNN with adaptive focal loss for automated ECG heartbeat classification.

Scientific reports·2026
Same author

Co-creation, co-design or co-production? Reflections on the development of urban health systems implementation strategies to improve access and quality of primary healthcare services in Bangladesh, Ghana, Nepal and Nigeria.

Health research policy and systems·2026
Same author

Review Article: Ileal Bile Acid Transport (IBAT) Inhibitors as an Emerging Treatment for Cholestatic Liver Disease.

Alimentary pharmacology & therapeutics·2026
Same author

Editorial: Cholestasis Is an Important Marker for Evaluating Clinical Outcomes and Prognosis in Severe Burns and Critically Ill Patients.

Alimentary pharmacology & therapeutics·2026
Same author

Evaluation and management of primary sclerosing cholangitis patients awaiting liver transplantation.

JHEP reports : innovation in hepatology·2026

Related Experiment Video

Updated: Feb 10, 2026

Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000
12:07

Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000

Published on: July 29, 2009

18.4K

Classification of Targets and Distractors Present in Visual Hemifields Using Time-Frequency Domain EEG Features.

Sweeti1, Deepak Joshi1, B K Panigrahi2

  • 1Centre for Biomedical Engineering, IIT Delhi, New Delhi, India.

Journal of Healthcare Engineering
|May 30, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces an electroencephalogram (EEG) classification system for visual cognitive load, achieving up to 87.2% accuracy in identifying targets and distractors. This work advances neurofeedback systems for visual attention.

More Related Videos

The Frequency Domain Thermoreflectance Technique for Thermal Property Measurements
09:10

The Frequency Domain Thermoreflectance Technique for Thermal Property Measurements

Published on: December 5, 2025

740
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

15.2K

Related Experiment Videos

Last Updated: Feb 10, 2026

Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000
12:07

Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000

Published on: July 29, 2009

18.4K
The Frequency Domain Thermoreflectance Technique for Thermal Property Measurements
09:10

The Frequency Domain Thermoreflectance Technique for Thermal Property Measurements

Published on: December 5, 2025

740
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

15.2K

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Biomedical Engineering

Background:

  • Cognitive load assessment is crucial for understanding attention.
  • Distinguishing between targets and distractors in visual fields is challenging.
  • Electroencephalogram (EEG) signals offer insights into neural activity during attention tasks.

Purpose of the Study:

  • To develop and validate a classification system for cognitive load related to visual targets and distractors.
  • To identify significant EEG signal features for accurate classification.
  • To explore the potential for a feature-based neurofeedback system for visual attention.

Main Methods:

  • Acquisition of EEG signals during a spatial attention task.
  • EEG feature selection based on distribution analysis.
  • Stepwise discriminant analysis (SDA) for channel selection.
  • Repeated measures analysis of variance (rANOVA) for statistical validation.
  • Development and comparison of classifiers using selected features.

Main Results:

  • Maximum classification accuracies of 87.2% and 86.1% were achieved.
  • Average classification accuracies were 76.5% ± 4% and 76.2% ± 5.3% across thirteen subjects.
  • Selected EEG features and channels proved statistically significant for classification.

Conclusions:

  • The developed classification system effectively differentiates cognitive load associated with visual targets and distractors.
  • The findings support the utility of EEG signal features for building neurofeedback systems.
  • This research represents a significant step towards real-time visual attention monitoring and enhancement.