Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Difference from Background: Limit of Detection

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

You might also read

Related Articles

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

Sort by

Same author

Editorial: Deep learning in brain-computer interfaces.

Frontiers in human neuroscience·2026

Same author

Kalmer, a specific based-App intervention for the treatment of Non-suicidal self-injury (NSSI): a technical and usability study in a non-clinical population.

Frontiers in digital health·2026

Same author

MCFANet: a multi-class fusion attention network for motor imagery EEG classification.

Frontiers in human neuroscience·2026

Same author

Clinical validation of artificial intelligence algorithms for the detection of different central-involved retinal pathologies and glaucoma from non-mydriatic images.

Frontiers in artificial intelligence·2026

Same author

A Smartphone-Based Psychological Intervention for Nonsuicidal Self-Injury (Kalmer App): Protocol for a Multicenter Double-Blind Randomized Controlled Trial.

JMIR research protocols·2026

Same author

Revealing Structural Brain-Cognition Relationships in Children: A Comparison of Morphometric Similarity and INverse Divergence Networks.

Neuroinformatics·2026

Search research articles

Related Experiment Video

Updated: Jul 8, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

Robust and memory-less median estimation for real-time spike detection.

Ariel Burman¹, Jordi Solé-Casals^2,3, Sergio E Lew¹

¹Instituto de Ingeniería Biomédica, Universidad de Buenos Aires, Buenos Aries, Argentina.

|November 26, 2024

Summary

This summary is machine-generated.

We developed a new 1-D median estimator for detecting threshold-crossing signals in neural recordings. This method significantly reduces variance and buffer size, improving power efficiency and response time.

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

Published on: February 10, 2017

Related Experiment Videos

Last Updated: Jul 8, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

Published on: February 10, 2017

Area of Science:

Signal Processing
Computational Neuroscience
Biomedical Engineering

Background:

Online detection of threshold-crossing signals is crucial for analyzing neural data.
Existing methods for spike detection in extracellular recordings face limitations in efficiency and accuracy.
Non-stationary signal analysis demands algorithms with rapid response times and low computational overhead.

Purpose of the Study:

To introduce a novel 1-D median estimator for real-time threshold-crossing signal detection.
To enhance the efficiency and performance of spike detection in neural recordings.
To reduce the resource requirements for online signal processing in neuroscience applications.

Main Methods:

Development of a specialized 1-D median estimator algorithm.
Comparative analysis against state-of-the-art algorithms using key performance metrics.
Evaluation of estimator variance, required buffer length, footprint area, and response time.

Main Results:

The proposed estimator reduces variance by up to eight times for a fixed buffer length.
Achieves a required buffer length up to eight times smaller for a given estimator variance.
Demonstrates a decrease in footprint area by over eight times, leading to lower power consumption.

Conclusions:

The novel 1-D median estimator offers significant improvements in efficiency and performance for online signal detection.
Reduced variance and buffer size translate to substantial gains in power efficiency and faster signal response.
This method is particularly advantageous for real-time analysis of neural signals, such as spike detection.