TY - GEN
T1 - Toward automated electrode selection in the electronic depth control strategy for multi-unit recordings
AU - Van Dijck, Gert
AU - Jezzini, Ahmad
AU - Herwik, Stanislav
AU - Kisban, Sebastian
AU - Seidl, Karsten
AU - Paul, Oliver
AU - Ruther, Patrick
AU - Serventi, Francesca Ugolotti
AU - Fogassi, Leonardo
AU - Van Hulle, Marc M.
AU - Umiltà, Maria Alessandra
N1 - Funding Information:
We thank Richard Csercsa, Peter Pazmany Catholic University, Budapest, for taking part in assigning quality scores to the neural signals. This research was performed within the framework of the Information Society Technologies (IST) Integrated Project NeuroProbes of the 6th Framework Program (FP6) of the European Commission (Project number IST-027017). Gert Van Dijck and Marc M. Van Hulle are sponsored by the CREA financing program (CREA/07/027) of the K.U. Leuven and the Belgian Fund for Scientific Research – Flanders (G.0588.09).
Funding Information:
Acknowledgments. We thank Richard Csercsa, Peter Pazmany Catholic University, Budapest, for taking part in assigning quality scores to the neural signals. This research was performed within the framework of the Information Society Technologies (IST) Integrated Project NeuroProbes of the 6th Framework Program (FP6) of the European Commission (Project number IST-027017). Gert Van Dijck and Marc M. Van Hulle are sponsored by the CREA financing program (CREA/07/027) of the K.U. Leuven and the Belgian Fund for Scientific Research – Flanders (G.0588.09).
PY - 2010
Y1 - 2010
N2 - Multi-electrode arrays contain an increasing number of electrodes. The manual selection of good quality signals among hundreds of electrodes becomes impracticable for experimental neuroscientists. This increases the need for an automated selection of electrodes containing good quality signals. To motivate the automated selection, three experimenters were asked to assign quality scores, taking one of four possible values, to recordings containing action potentials obtained from the monkey primary somatosensory cortex and the superior parietal lobule. Krippendorff's alpha-reliability was then used to verify whether the scores, given by different experimenters, were in agreement. A Gaussian process classifier was used to automate the prediction of the signal quality using the scores of the different experimenters. Prediction accuracies of the Gaussian process classifier are about 80% when the quality scores of different experimenters are combined, through a median vote, to train the Gaussian process classifier. It was found that predictions based also on firing rate features are in closer agreement with the experimenters' assignments than those based on the signal-to-noise ratio alone.
AB - Multi-electrode arrays contain an increasing number of electrodes. The manual selection of good quality signals among hundreds of electrodes becomes impracticable for experimental neuroscientists. This increases the need for an automated selection of electrodes containing good quality signals. To motivate the automated selection, three experimenters were asked to assign quality scores, taking one of four possible values, to recordings containing action potentials obtained from the monkey primary somatosensory cortex and the superior parietal lobule. Krippendorff's alpha-reliability was then used to verify whether the scores, given by different experimenters, were in agreement. A Gaussian process classifier was used to automate the prediction of the signal quality using the scores of the different experimenters. Prediction accuracies of the Gaussian process classifier are about 80% when the quality scores of different experimenters are combined, through a median vote, to train the Gaussian process classifier. It was found that predictions based also on firing rate features are in closer agreement with the experimenters' assignments than those based on the signal-to-noise ratio alone.
KW - Continuous wavelet transform
KW - Electronic depth control
KW - Gaussian process classifier
KW - Inter-rater reliability
KW - Multi-unit recordings
KW - Spike detection
UR - http://www.scopus.com/inward/record.url?scp=78650206601&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-17534-3_3
DO - 10.1007/978-3-642-17534-3_3
M3 - Conference contribution
AN - SCOPUS:78650206601
SN - 3642175333
SN - 9783642175336
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 17
EP - 25
BT - Neural Information Processing
Y2 - 22 November 2010 through 25 November 2010
ER -