In this section
@inproceedings{Dávila-Montero:2017:10.1109/ISCAS.2017.8050425,
author = {Dávila-Montero, S and Barsakcioglu, DY and Jackson, A and Constandinou, TG and Mason, AJ},
doi = {10.1109/ISCAS.2017.8050425},
pages = {690--693},
publisher = {IEEE},
title = {Real-time clustering algorithm that adapts to dynamic changes in neural recordings},
url = {http://dx.doi.org/10.1109/ISCAS.2017.8050425},
year = {2017}
}
TY - CPAPER
AB - This work presents a computationally efficient real-time adaptive clustering algorithm that recognizes and adapts to dynamic changes observed in neural recordings. The algorithm consists of an off-line training phase that determines initial cluster positions, and an on-line operation phase that continuously tracks drifts in clusters and periodically verifies acute changes in cluster composition. Analysis of chronic recordings from non-human primates shows that adaptive clustering achieves an improvement of 14% in classification accuracy and demonstrates an ability to recognize acute changes with 78% accuracy, with up to 29% computational efficiency compared to the state-of-the-art. The presented algorithm is suitable for long-term chronic monitoring of neural activity in various applications such as neuroscience research and control of neural prosthetics and assistive devices.
AU - Dávila-Montero,S
AU - Barsakcioglu,DY
AU - Jackson,A
AU - Constandinou,TG
AU - Mason,AJ
DO - 10.1109/ISCAS.2017.8050425
EP - 693
PB - IEEE
PY - 2017///
SP - 690
TI - Real-time clustering algorithm that adapts to dynamic changes in neural recordings
UR - http://dx.doi.org/10.1109/ISCAS.2017.8050425
UR - http://hdl.handle.net/10044/1/46109
ER -