The analysis of stimulus evoked potentials recorded using high resolution chips are very useful in understanding brain activity with greater details. However, the stimulus induced signals are often contaminated with stimulus artifacts. The artifact removal technique here discussed removes all such contaminations caused by the stimuli. The usage of peak-valley detection in estimating the artifact signature provides the benefit of removing these unwanted artifacts from the real neuronal recordings, diminishing the barrier of artifact shape and duration imposed by many existing techniques. Also, this technique provides the flexibility of automatic batch processing of neuronal signals. The artifact signature is estimated through the detection of peaks-and-valleys based on a threshold calculated using the signal’s standard deviation, thus overcoming the manual threshold selection. This technique provides the advantages of being simple, straightforward, and computationally efficient. The peak-valley detection approach has been demonstrated to be an efficient and accurate artifact and offset (baseline correction) removal method, as validated by analyzing high-resolution recordings from the rat somatosensory cortex (S1).

Slow Stimulus Artifact Removal through Peak-Valley Detection of Neuronal Signals Recorded from Somatosensory Cortex by High Resolution Brain-Chip Interface

MAHMUD, MUFTI;GIRARDI, STEFANO;MASCHIETTO, MARTA;RAHMAN, MOHAMMED MOSTAFIZUR;BERTOLDO, ALESSANDRA;VASSANELLI, STEFANO
2009

Abstract

The analysis of stimulus evoked potentials recorded using high resolution chips are very useful in understanding brain activity with greater details. However, the stimulus induced signals are often contaminated with stimulus artifacts. The artifact removal technique here discussed removes all such contaminations caused by the stimuli. The usage of peak-valley detection in estimating the artifact signature provides the benefit of removing these unwanted artifacts from the real neuronal recordings, diminishing the barrier of artifact shape and duration imposed by many existing techniques. Also, this technique provides the flexibility of automatic batch processing of neuronal signals. The artifact signature is estimated through the detection of peaks-and-valleys based on a threshold calculated using the signal’s standard deviation, thus overcoming the manual threshold selection. This technique provides the advantages of being simple, straightforward, and computationally efficient. The peak-valley detection approach has been demonstrated to be an efficient and accurate artifact and offset (baseline correction) removal method, as validated by analyzing high-resolution recordings from the rat somatosensory cortex (S1).
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/2487670
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