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Sampling Continuous Time Signal

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
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Basic Continuous Time Signals

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Related Experiment Video

Updated: May 14, 2026

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

Published on: March 10, 2026

Mushu, a free- and open source BCI signal acquisition, written in Python.

Bastian Venthur¹, Benjamin Blankertz

¹Berlin Institute of Technology, Franklinstr. 28/29, 10587 Berlin, Germany. bastian.venthur@tu-berlin.de

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|February 1, 2013

Summary

Mushu software streamlines Electroencephalography (EEG) data acquisition for Brain Computer Interfacing (BCI). This free, open-source tool offers a unified interface for diverse EEG amplifiers across major operating systems.

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Area of Science:

Neuroscience
Computer Science
Biomedical Engineering

Background:

Electroencephalography (EEG) data acquisition often requires specialized software for different hardware amplifiers.
Brain Computer Interfacing (BCI) research necessitates efficient and reliable real-time EEG signal processing.
Lack of a unified interface complicates data retrieval and integration across various EEG systems.

Purpose of the Study:

To introduce Mushu, a novel signal acquisition software designed for EEG data retrieval and online streaming.
To provide a unified software interface for EEG data, irrespective of the amplifier hardware used.
To support the development and application of Brain Computer Interfacing (BCI) technologies.

Main Methods:

Mushu is developed in Python, ensuring cross-platform compatibility (Windows, macOS, Linux).
The software focuses on signal acquisition, data retrieval, and real-time streaming capabilities.
It is designed as free and open-source software under the GNU General Public License.

Main Results:

Mushu offers a unified interface, simplifying the process of working with EEG data from various amplifiers.
The software facilitates efficient online streaming of EEG signals, crucial for real-time BCI applications.
Its cross-platform nature enhances accessibility for researchers and developers worldwide.

Conclusions:

Mushu provides a versatile and accessible solution for EEG signal acquisition and processing.
The software's unified interface and open-source nature promote wider adoption in BCI research and development.
Mushu contributes to advancing neuroscience and BCI by simplifying data handling and promoting collaboration.