Abstract-Multi-sensor extracellular recording takes advantage of several electrode channels to record from multiple neurons at the same time. However, the resulting low signal-tonoise ratio (SNR) combined with biological noise makes signal detection, the first step of any neurophysiological data analysis, difficult. A matched filter was therefore designed to better detect extracellular action potentials (EAPs) from multi-sensor extracellular recordings. The detector was tested on tetrode data from a locust antennal lobe and assessed against three trained analysts. 25 EAPs and noise samples were selected manually from the data and used for training. To reduce complexity, the filter assumed that the underlying noise in the data was spatially white. The detector performed with an average TP and FP rate of 84.62% and 16.63% respectively. This high level of performance indicates the algorithm is suitable for widespread use.

Abstract-Linear approaches to low-dimensional feature extraction may not be appropriate when statistical data are generated by a nonlinear interaction of parameters. Equally inadequate are linear methods for determining the dimension of the feature space. This article estimates the intrinsic dimension of extracellular action potentials (EAPs), which can be viewed as the minimum number of nonlinearly interacting parameters sufficient to describe the data. When combined with nonlinear feature extraction methods, this information may lead to a more faithful, low-dimensional EAP representation. These points are demonstrated using EAPs recorded experimentally by a multisensor electrode.