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

Lung Capacity01:47

Lung Capacity

56.3K
The air in the lungs is measured in volumes and capacities. Lung volume measures reflect the amount of air taken in, released, or left over after a lung function, like a single inhalation. Lung capacity measures are sums of two or more lung volume measures.
56.3K
Increasing Function01:18

Increasing Function

394
An increasing function exhibits a rise in output values as input values increase. This behavior is depicted graphically as a curve or line that slopes upward from left to right. Such a function satisfies the condition that if x1 < x2, then f(x1) < f(x2), indicating that the function values grow with increasing inputs. This concept is fundamental in understanding growth trends across various domains, such as population dynamics, financial investments, or resource consumption.The...
394
Respiratory Capacities01:24

Respiratory Capacities

1.4K
Respiratory capacities are crucial indicators of lung function, representing the maximum amount of air an individual's respiratory system can handle during various breathing phases.
One key metric is the Inspiratory Capacity (IC), which represents the maximum amount of air that can be inhaled with full effort. IC is calculated by summing the tidal volume and inspiratory reserve volume, typically ranging from 2.4 to 3.6 liters.
The Functional Residual Capacity (FRC) represents the air in the...
1.4K
Increased Body Temperature01:25

Increased Body Temperature

7.5K
A body temperature above  38°C  (100.4 °F) is known as fever or pyrexia, and a person with fever is termed 'febrile.' Typically, the hypothalamus, a part of the brain that acts as the body's thermostat, regulates body temperature through a thermoregulatory setpoint. It receives signals from cold and warm thermal receptors throughout the body and adjusts the body's temperature accordingly. Fever occurs when this hypothalamic setpoint is altered, usually in...
7.5K
Increased pulse rate01:17

Increased pulse rate

1.2K
Tachycardia is a condition marked by an abnormally fast or irregular heart rate, surpassing the typical resting rate. In adults, tachycardia is characterized by a pulse rate ranging from 100 to 180 beats per minute. The increased heart rate can result in inadequate blood flow to various body parts, ultimately diminishing the oxygen supply to organs and tissues.
Many factors can elevate the risk of developing tachycardia. These include advanced age, a family history of arrhythmias, and an...
1.2K
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K

You might also read

Related Articles

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

Sort by
Same author

Bioinformatics-Based Research on Key Genes and Pathways of Intervertebral Disc Degeneration.

Cartilage·2020
Same author

Characterization of IL-22 Bioactivity and IL-22-Positive Cells in Grass Carp <i>Ctenopharyngodon idella</i>.

Frontiers in immunology·2020
Same author

Triboelectric and Piezoelectric Nanogenerators for Future Soft Robots and Machines.

iScience·2020
Same author

The evolution and functional characterization of CXC chemokines and receptors in lamprey.

Developmental and comparative immunology·2020
Same author

Identification and external validation of the optimal FIB-4 and APRI thresholds for ruling in chronic hepatitis B related liver fibrosis in tertiary care settings.

Journal of clinical laboratory analysis·2020
Same author

Characteristics of Osteoporotic Low Lumbar Vertebral Fracture and Related Lumbosacral Sagittal Imbalance.

Orthopedics·2020

Related Experiment Video

Updated: Feb 2, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

737

Increasing the learning Capacity of BCI Systems via CNN-HMM models.

Yashas Malur Saidutta, Jun Zou, Faramarz Fekri

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |November 17, 2018
    PubMed
    Summary

    This study introduces a hybrid Convolutional Neural Network-Hidden Markov Model (CNN-HMM) to enhance Brain Computer Interface (BCI) command detection. The novel approach significantly boosts accuracy and expands the number of usable commands.

    More Related Videos

    Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems
    08:42

    Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems

    Published on: May 5, 2015

    12.6K
    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    2.5K

    Related Experiment Videos

    Last Updated: Feb 2, 2026

    Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
    07:13

    Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

    Published on: April 18, 2025

    737
    Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems
    08:42

    Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems

    Published on: May 5, 2015

    12.6K
    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    2.5K

    Area of Science:

    • Neuroscience
    • Computer Science
    • Biomedical Engineering

    Background:

    • Brain Computer Interfaces (BCIs) face limitations in command detection accuracy and quantity, hindering widespread adoption.
    • Current BCI systems struggle to increase the number of commands without sacrificing performance.

    Purpose of the Study:

    • To propose a hybrid Convolutional Neural Network-Hidden Markov Model (CNN-HMM) system to increase the number of detectable commands in BCIs.
    • To maintain or improve command detection accuracy while expanding the command set size.

    Main Methods:

    • A hybrid CNN-HMM model was developed, utilizing a CNN classifier for basic mental tasks and HMMs for sequence detection.
    • A subset of sequences was selected by measuring distances between HMM models to optimize learning capacity.
    • Experiments were conducted to evaluate the performance against non-sequenced classifiers.

    Main Results:

    • The CNN-HMM system demonstrated a 14% gain in accuracy compared to non-sequenced classifiers.
    • The system can increase the command set size by 4 times (using all channels) or 1.5 times (using 1/3 channels) while maintaining comparable performance to non-sequenced classifiers using all channels.

    Conclusions:

    • The hybrid CNN-HMM model is a viable approach for enhancing the learning capacity of BCIs.
    • This method effectively addresses the limitations of command quantity and accuracy in current BCI technology.