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Extracellular action potentials (EAPs) must be classified before they can yield any useful information on neuronal function and organization. Neuronal source classification therefore represents a critical step in the analysis of electrophysiological data. This study demonstrates the efficacy of a multi-sensor EAP classification scheme using source location as a classification feature. Localization...
Signal detection represents the first processing step in the analysis of extracellular action potentials (EAPs). By combining the theory of wavelets with statistical signal detection, we derived an approximation of the generalized matched filter that is suitable for semi-supervised (noise known, signal unknown) detection of EAPs in multisensor recordings. When tested on experimental data recorded...
Electrocorticogram (ECoG)-based brain computer interfaces (BCI) can potentially be used for control of arm prostheses. Restoring independent function to BCI users with such a system will likely require control of many degrees-of-freedom (DOF). However, our ability to decode many-DOF arm movements from ECoG signals has not been thoroughly tested. To this end, we conducted a comprehensive study of the...
Four-sensor microelectrodes, commonly referred to as tetrodes, have the ability to significantly increase the signal-to-noise ratio of neuronal extracellular recordings. They also provide spatio-temporal information about extracellular action potentials (EAP) which may be used to localize and resolve individual neuronal signal sources. Since the relative position of sensors and neurons whose EAPs...
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