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Real-time clustering algorithm that adapts to dynamic changes in neural recordings

Lookup NU author(s): Professor Andrew Jackson

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Abstract

© 2017 IEEE. 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 significantly improved computational efficiency compared to the state-of-the-art. The presented algorithm is suitable for long-term chronic monitoring of neural activity in many applications of neuroscience research and control of neural prosthetics and assistive devices.


Publication metadata

Author(s): Davila-Montero S, Barsakcioglu DY, Jackson A, Constandinou TG, Mason AJ

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: IEEE International Symposium on Circuits and Systems (ISCAS)

Year of Conference: 2017

Online publication date: 28/09/2017

Acceptance date: 02/04/2016

Publisher: Institute of Electrical and Electronics Engineers Inc.

URL: https://doi.org/10.1109/ISCAS.2017.8050425

DOI: 10.1109/ISCAS.2017.8050425

Library holdings: Search Newcastle University Library for this item

ISBN: 9781467368520


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