TY - GEN
T1 - High performance spike detection and sorting using neural waveform phase information and SOM clustering
AU - Yang, Chenhui
AU - Yuan, Yuan
AU - Si, Jennie
PY - 2010
Y1 - 2010
N2 - Neural spike detection is the very first step in the analysis of recorded neural waveforms for brain machine interface applications and for neuroscientific studies. Spike detection accuracy and algorithm robustness is an important consideration in developing detection algorithms. For real neural recording data without respective ground truth, the evaluation of detection performance is a challenge. In the present paper we evaluate the detections by inspecting the detected spike waveforms for their compliance with neural spike electrophysiological properties. After classifying similar waveforms into one cluster, those qualified detections are determined to be spikes with high confidence. This new spike detection evaluation method is based on using the waveform phase information for cluster analysis. By including clustering as an integral step in the detection algorithm, we can refine detection results and improve detection performance. The new algorithm is easy to implement and is effective as demonstrated using both artificial and real neural waveforms.
AB - Neural spike detection is the very first step in the analysis of recorded neural waveforms for brain machine interface applications and for neuroscientific studies. Spike detection accuracy and algorithm robustness is an important consideration in developing detection algorithms. For real neural recording data without respective ground truth, the evaluation of detection performance is a challenge. In the present paper we evaluate the detections by inspecting the detected spike waveforms for their compliance with neural spike electrophysiological properties. After classifying similar waveforms into one cluster, those qualified detections are determined to be spikes with high confidence. This new spike detection evaluation method is based on using the waveform phase information for cluster analysis. By including clustering as an integral step in the detection algorithm, we can refine detection results and improve detection performance. The new algorithm is easy to implement and is effective as demonstrated using both artificial and real neural waveforms.
UR - http://www.scopus.com/inward/record.url?scp=79959473875&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79959473875&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2010.5596908
DO - 10.1109/IJCNN.2010.5596908
M3 - Conference contribution
AN - SCOPUS:79959473875
SN - 9781424469178
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
Y2 - 18 July 2010 through 23 July 2010
ER -