A deep neural network-based spike sorting with improved channel selection and artefact removal
A deep neural network-based spike sorting with improved channel selection and artefact removal
In order to implement highly efficient brain-machine interface (BMI) systems, high-channel count sensing is often used to record extracellular action potentials. However, the extracellular recordings are typically severely contaminated by artefacts and various noise sources, rendering the separation of multi-unit neural recordings an immensely challenging task. Removing artefact and noise from neural events can improve the spike sorting performance and classification accuracy. This paper presents a deep learning technique called deep spike detection (DSD) with a strong learning ability of high-dimensional vectors for neural channel selection and artefacts removal from the selected neural channel. The proposed method significantly improves spike detection compared to the conventional methods by sequentially diminishing the noise level and discarding the active artefacts in the recording channels. The simulated and experimental results show that there is considerably better performance when the extracellular raw recordings are cleaned prior to assigning individual spikes to the neurons that generated them. The DSD achieves an overall classification accuracy of 91.53% and outperformes Wave_clus by 3.38% on the simulated dataset with various noise levels and artefacts.
Artefact removal, channel selection, convolutional neural network (CNN), deep learning, deep spike detection (DSD), extracellular recordings, real-time sorting, spike sorting
15131-15143
Okreghe, Christian O.
91754d88-d49c-4bef-85a4-07ec9869b902
Zamani, Majid
431788cc-0702-4fa9-9709-f5777a2d0d25
Demosthenous, Andreas
bed19531-d770-4f48-8464-59d225ddea8d
17 February 2023
Okreghe, Christian O.
91754d88-d49c-4bef-85a4-07ec9869b902
Zamani, Majid
431788cc-0702-4fa9-9709-f5777a2d0d25
Demosthenous, Andreas
bed19531-d770-4f48-8464-59d225ddea8d
Okreghe, Christian O., Zamani, Majid and Demosthenous, Andreas
(2023)
A deep neural network-based spike sorting with improved channel selection and artefact removal.
IEEE Access, 11, .
(doi:10.1109/ACCESS.2023.3242643).
Abstract
In order to implement highly efficient brain-machine interface (BMI) systems, high-channel count sensing is often used to record extracellular action potentials. However, the extracellular recordings are typically severely contaminated by artefacts and various noise sources, rendering the separation of multi-unit neural recordings an immensely challenging task. Removing artefact and noise from neural events can improve the spike sorting performance and classification accuracy. This paper presents a deep learning technique called deep spike detection (DSD) with a strong learning ability of high-dimensional vectors for neural channel selection and artefacts removal from the selected neural channel. The proposed method significantly improves spike detection compared to the conventional methods by sequentially diminishing the noise level and discarding the active artefacts in the recording channels. The simulated and experimental results show that there is considerably better performance when the extracellular raw recordings are cleaned prior to assigning individual spikes to the neurons that generated them. The DSD achieves an overall classification accuracy of 91.53% and outperformes Wave_clus by 3.38% on the simulated dataset with various noise levels and artefacts.
Text
A_Deep_Neural_Network-Based_Spike_Sorting_With_Improved_Channel_Selection_and_Artefact_Removal
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More information
Accepted/In Press date: 26 January 2023
e-pub ahead of print date: 3 February 2023
Published date: 17 February 2023
Keywords:
Artefact removal, channel selection, convolutional neural network (CNN), deep learning, deep spike detection (DSD), extracellular recordings, real-time sorting, spike sorting
Identifiers
Local EPrints ID: 489292
URI: http://eprints.soton.ac.uk/id/eprint/489292
ISSN: 2169-3536
PURE UUID: eb4ec6fc-96c9-424c-956c-580738ce22d8
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Date deposited: 19 Apr 2024 16:36
Last modified: 06 Jun 2024 02:19
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Contributors
Author:
Christian O. Okreghe
Author:
Majid Zamani
Author:
Andreas Demosthenous
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