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Bayesian signal detection and source...

Mubeen, Muhammad Asim.

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Bayesian signal detection and source separation in simulated brain computer interface systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bayesian signal detection and source separation in simulated brain computer interface systems./
作者:	Mubeen, Muhammad Asim.
面頁冊數:	147 p.
附註:	Source: Dissertation Abstracts International, Volume: 77-10(E), Section: B.
Contained By:	Dissertation Abstracts International77-10B(E).
標題:	Biomedical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10109594
ISBN:	9781339728780

Bayesian signal detection and source separation in simulated brain computer interface systems.
Mubeen, Muhammad Asim.

Bayesian signal detection and source separation in simulated brain computer interface systems. - 147 p.

Source: Dissertation Abstracts International, Volume: 77-10(E), Section: B.

Thesis (Ph.D.)--State University of New York at Albany, 2016.

The problems of signal detection and source separation are important in many fields of science and engineering. In many cases, a target signal needs to be detected in real time and is contaminated by noise. Sometimes the level of noise is on the order of the signal itself. The real time detection of a target signal is of key importance in problems such as the brain computer interface systems. In brain computer interface systems, the neural activity (electric signals) of the brain is detected using sensors (electrodes) on the surface of the brain or the scalp. This signal is contaminated by various types of noise. The level of contamination increases when signal is recorded non-invasively. To detect such signals of interest a Bayesian signal detection technique has been developed and tested for various noise levels and compared with the popular technique of cross-correlation. Receiver operator curves (ROC) are employed to test the robustness of the proposed method and for comparison purposes.

ISBN: 9781339728780Subjects--Topical Terms:

535387
Biomedical engineering.

Bayesian signal detection and source separation in simulated brain computer interface systems.
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520 $a The problems of signal detection and source separation are important in many fields of science and engineering. In many cases, a target signal needs to be detected in real time and is contaminated by noise. Sometimes the level of noise is on the order of the signal itself. The real time detection of a target signal is of key importance in problems such as the brain computer interface systems. In brain computer interface systems, the neural activity (electric signals) of the brain is detected using sensors (electrodes) on the surface of the brain or the scalp. This signal is contaminated by various types of noise. The level of contamination increases when signal is recorded non-invasively. To detect such signals of interest a Bayesian signal detection technique has been developed and tested for various noise levels and compared with the popular technique of cross-correlation. Receiver operator curves (ROC) are employed to test the robustness of the proposed method and for comparison purposes.
520 $a The separation of mixed signals, also known as source separation, is another important problem in various areas of science and engineering. In this problem, when trying to detect some specific kind of signal using a sensor, the signal recorded at the sensor is corrupted by various other similar unwanted signals and the recorded signal needs to be resolved by separating the unwanted signals. In the case of non-invasive brain computer interface systems, signals are recorded at various locations on the human scalp using electrodes (sensors). The signal recorded by a sensor is a combination of neural activities at various locations in the brain. In this study, a Bayesian source separation technique for low frequency signals based on cubic spline models has been developed and tested against some popular source separation techniques. A comparison has been performed using source-to-noise ratio measures.
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