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Cognitive Computation

Erschienen in:

01.02.2015

Embedded Implementation of Second-Order Blind Identification (SOBI) for Real-Time Applications in Neuroscience

verfasst von: Xun Zhang, François-Benoît Vialatte, Chen Chen, Apurva Rathi, Gérard Dreyfus

Erschienen in: Cognitive Computation | Ausgabe 1/2015

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Abstract

Blind source separation (BSS) is an effective and powerful tool for signal processing and artifact removal in electroencephalographic signals. For real-time applications such as brain–computer interfaces, cognitive neuroscience or clinical neuromonitoring, it is of prime importance that BSS is effectively performed in real time. In order to improve in terms of speed considering the optimal parallelism environment that hardware provides, we build a high-level hardware/software co-simulation based on MATLAB/Simulink for BSS application. To illustrate our approach, we implement the most critical parts of the second-order blind identification algorithm with a fixed-point algorithm on a commercial field-programmable gate array development kit. The results obtained show that co-simulation environment reduces the computation time from 1.9 s to 12.8 ns and thus has great potential for real-time applications.

Vorheriger Artikel On Automatic Diagnosis of Alzheimer’s Disease Based on Spontaneous Speech Analysis and Emotional Temperature

Nächster Artikel Model of the Reticular Formation of the Brainstem Based on Glial–Neuronal Interactions

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Titel: Embedded Implementation of Second-Order Blind Identification (SOBI) for Real-Time Applications in Neuroscience
verfasst von: Xun Zhang
François-Benoît Vialatte
Chen Chen
Apurva Rathi
Gérard Dreyfus
Publikationsdatum: 01.02.2015
Verlag: Springer US
Erschienen in: Cognitive Computation / Ausgabe 1/2015
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI: https://doi.org/10.1007/s12559-014-9282-z

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