Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Aliasing01:18

Aliasing

143
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
143
Upsampling01:22

Upsampling

240
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
240
Fast Fourier Transform01:10

Fast Fourier Transform

348
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
348
Bandpass Sampling01:17

Bandpass Sampling

187
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
187
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

209
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
209
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

280
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
280

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Cell-based cytokine patch for localized immunomodulation and accelerated healing in rodent and porcine wounds.

Nature biomedical engineering·2026
Same author

A minimally invasive floating-wire interface for transcranial deep brain stimulation.

Brain stimulation·2026
Same author

Microimager: a flexible thin-film miniaturized endoscope for optical biomedical imaging.

Biomedical optics express·2026
Same author

Smart Dura: a functional artificial dura for multi-modal neural recording and modulation.

Microsystems & nanoengineering·2026
Same author

Multimodal Optical Imaging and Modulation with Simultaneous Electrophysiology Through Smart Dura in Non-Human Primates.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Robust minimally-invasive microfabricated stainless steel neural interfaces for high resolution recording.

Nature communications·2026

相关实验视频

Updated: Jul 11, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K

适应频域过用于神经信号预处理.

Esther Bedoyan1, Jay W Reddy2, Anna Kalmykov3

  • 1Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

NeuroImage
|November 3, 2023
PubMed
概括

这项研究引入了一种自适应方法,自动删除神经记录中的电干扰. 该技术提高了信号质量,以便更准确地分析大脑活动.

更多相关视频

High Density Event-related Potential Data Acquisition in Cognitive Neuroscience
08:33

High Density Event-related Potential Data Acquisition in Cognitive Neuroscience

Published on: April 16, 2010

12.7K
Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

1.9K

相关实验视频

Last Updated: Jul 11, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K
High Density Event-related Potential Data Acquisition in Cognitive Neuroscience
08:33

High Density Event-related Potential Data Acquisition in Cognitive Neuroscience

Published on: April 16, 2010

12.7K
Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

1.9K

科学领域:

  • 神经科学是一个神经科学.
  • 信号处理 信号处理
  • 生物医学工程 生物医学工程

背景情况:

  • 电干扰是多电极阵列 (MEA) 神经记录的一个重大挑战,降低了信号噪声比 (SNR) 并阻碍了精确的尖端分类.
  • 像带通和隙过这样的传统方法需要对干扰频率的预先了解,这限制了它们对各种实验设置的适应性.

研究的目的:

  • 开发和验证一种自适应后处理方法,用于自动检测和从细胞外电生理学数据中移除窄带电干扰.
  • 评估拟议方法在保护神经信号完整性,同时减轻噪声方面的有效性.

主要方法:

  • 实施了自适应的光谱峰值检测和移除 (SPDR) 方法,根据光谱峰值突出度 (SPP) 值识别干扰.
  • 频域中的干扰峰值使用隙过来消除.
  • 该方法在模拟数据和来自大脑器官的实验记录上进行了测试,结果与光光成像相比较.

主要成果:

  • 该SPDR方法成功地从电生理学记录中消除了不必要的电干扰,而不会显著扭曲神经信号.
  • 使用成像的验证证实了最小的信号扭曲,为优化SPP值以改善SNR提供了边界.
  • 适应性过技术在自动识别和消除带间干扰方面表现出强大.

结论:

  • 拟议的自适应过技术为缓解神经记录中的电干扰提供了一个强大的,自动化的解决方案.
  • 这种方法有可能提高数据质量,并在更具挑战性的自然环境中实现神经记录.
  • 仔细选择SPP值至关重要,以平衡干扰消除和信号保真.