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Lookup NU author(s): Professor Andrew Jackson
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© 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.
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.
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