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

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Title: Real-time clustering algorithm that adapts to dynamic changes in neural recordings
Authors: Dávila-Montero, S
Barsakcioglu, DY
Jackson, A
Constandinou, TG
Mason, AJ
Item Type: Conference Paper
Abstract: 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.
Issue Date: 28-May-2017
Date of Acceptance: 17-Feb-2017
URI: http://hdl.handle.net/10044/1/46109
Publisher: IEEE
Start Page: 690
End Page: 693
Copyright Statement: This paper is embargoed until publication
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/I000569/1
EP/M020975/1
EESA_P59880
Conference Name: IEEE International Symposium on Circuits & Systems (ISCAS)
Publication Status: Accepted
Start Date: 2017-05-28
Finish Date: 2017-05-31
Conference Place: Baltimore, MD (USA)
Embargo Date: publication subject to indefinite embargo
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering



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