Year: 2009
Type: book-chapter
Abstract: Studying signal and noise properties of recorded neural data is critical in developing more efficient algorithms to recover the encoded information. Important \nissues exist in this research including the variant spectrum spans of neural spikes that make it difficult to choose a globally optimal bandpass filter. Also, multiple \nsources produce aggregated noise that deviates from the conventional white Gaussian noise. In this work, the spectrum variability of spikes is addressed, based on \nwhich the concept of adaptive bandpass filter that fits the spectrum of individual spikes is proposed. Multiple noise sources have been studied through analytical \nmodels as well as empirical measurements. The dominant noise source is identified as neuron noise followed by interface noise of the electrode. This suggests \nthat major efforts to reduce noise from electronics are not well spent. The measured noise from in vivo experiments shows a family of 1/f^x spectrum that can be reduced using noise shaping techniques. In summary, the methods of adaptive bandpass filtering and noise shaping together result in several dB signal-to-noise ratio (SNR) enhancement.
Authors: Zhi Yang, Qi Zhao, Edward W. Keefer, Wentai Liu
Language: English
Subfield: Cellular and Molecular Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2143428314