Realtime bioelectrical data acquisition and processing from 128 channels utilizing the wavelet-transformation
| Autor: | Thomas Malina, Ulrich G. Hofmann, A. Folkers, Florian Mösch |
|---|---|
| Rok vydání: | 2003 |
| Předmět: |
Lossless compression
Discrete wavelet transform Signal processing Digital signal processor business.industry Computer science Cognitive Neuroscience Low-pass filter Stationary wavelet transform Second-generation wavelet transform Noise reduction Speech recognition Pattern recognition Data_CODINGANDINFORMATIONTHEORY Lossy compression Computer Science Applications Wavelet packet decomposition Multidimensional signal processing Wavelet Artificial Intelligence Artificial intelligence Fast wavelet transform business Continuous wavelet transform Digital signal processing |
| Zdroj: | Neurocomputing. :247-254 |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/s0925-2312(02)00763-4 |
| Popis: | We propose a versatile signal processing and analysis framework for bioelectrical data, and in particular for neural recordings and EEG. Within this framework the signal is decomposed into subbands using fast wavelet transform algorithms, executed in real-time on a current digital signal processor hardware platform. The decomposition is used to perform various processing and analysis tasks. Besides fast implementation of high, band, and low pass filters, the decomposition is used for denoising and lossy, as well as lossless compression. Furthermore specific electrophysiologic analysis tasks like spike detection and sorting are performed within this decomposition scheme. |
| Databáze: | OpenAIRE |
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