Computational NeuroscienceExtracellular spike detection from multiple electrode array using novel intelligent filter and ensemble fuzzy decision making

- Neuronal data are used in many scientific and clinical applications.
- Spike detection methods are needed to estimate the time instants of action potentials.
- We suggest utilizing a novel approach to choose the filter parameters automatically.
- Hilbert transform is employed as a pre-processing step.
- We propose two novel approaches to combine some existing spike detectors.

BackgroundThe information obtained from signal recorded with extracellular electrodes is essential in many research fields with scientific and clinical applications. These signals are usually considered as a point process and a spike detection method is needed to estimate the time instants of action potentials. In order to do so, several steps are taken but they all depend on the results of the first step, which filters the signals. To alleviate the effect of noise, selecting the filter parameters is very time-consuming. In addition, spike detection algorithms are signal dependent and their performance varies significantly when the data change.New methodsWe propose two approaches to tackle the two problems above. We employ ensemble empirical mode decomposition (EEMD), which does not require parameter selection, and a novel approach to choose the filter parameters automatically. Then, to boost the efficiency of each of the existing methods, the Hilbert transform is employed as a pre-processing step. To tackle the second problem, two novel approaches, which use the fuzzy and probability theories to combine a number of spike detectors, are employed to achieve higher performance.Results, comparison with existing method(s) and conclusionsThe simulation results for realistic synthetic and real neuronal data reveal the improvement of the proposed spike detection techniques over state-of-the art approaches. We expect these improve subsequent steps like spike sorting.