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Huang L, Gan L, Zeng Y, Ling BWK. Automatical Spike Sorting With Low-Rank and Sparse Representation. IEEE Trans Biomed Eng 2024; 71:1677-1686. [PMID: 38147418 DOI: 10.1109/tbme.2023.3347137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
Spikesorting is crucial in studying neural individually and synergistically encoding and decoding behaviors. However, existent spike sorting algorithms perform unsatisfactorily in real scenarios where heavy noises and overlapping samples are commonly in the spikes, and the spikes from different neurons are similar. To address such challenging scenarios, we propose an automatic spike sporting method in this paper, which integrally combines low-rank and sparse representation (LRSR) into a unified model. In particular, LRSR models spikes through low-rank optimization, uncovering global data structure for handling similar and overlapped samples. To eliminate the influence of the embedded noises, LRSR uses a sparse constraint, effectively separating spikes from noise. The optimization is solved using alternate augmented Lagrange multipliers methods. Moreover, we conclude with an automatic spike-sorting framework that employs the spectral clustering theorem to estimate the number of neurons. Extensive experiments over various simulated and real-world datasets demonstrate that our proposed method, LRSR, can handle spike sorting effectively and efficiently.
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Jia Q, Liu Y, Lv S, Wang Y, Jiao P, Xu W, Xu Z, Wang M, Cai X. Wireless closed-loop deep brain stimulation using microelectrode array probes. J Zhejiang Univ Sci B 2024:1-21. [PMID: 38423536 DOI: 10.1631/jzus.b2300400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 08/25/2023] [Indexed: 03/02/2024]
Abstract
Deep brain stimulation (DBS), including optical stimulation and electrical stimulation, has been demonstrated considerable value in exploring pathological brain activity and developing treatments for neural disorders. Advances in DBS microsystems based on implantable microelectrode array (MEA) probes have opened up new opportunities for closed-loop DBS (CL-DBS) in situ. This technology can be used to detect damaged brain circuits and test the therapeutic potential for modulating the output of these circuits in a variety of diseases simultaneously. Despite the success and rapid utilization of MEA probe-based CL-DBS microsystems, key challenges, including excessive wired communication, need to be urgently resolved. In this review, we considered recent advances in MEA probe-based wireless CL-DBS microsystems and outlined the major issues and promising prospects in this field. This technology has the potential to offer novel therapeutic options for psychiatric disorders in the future.
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Affiliation(s)
- Qianli Jia
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yaoyao Liu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shiya Lv
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiding Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Peiyao Jiao
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Xu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhaojie Xu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mixia Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China.
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Xinxia Cai
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China. ,
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China. ,
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Yu H, Qi Y, Pan G. NeuSort: an automatic adaptive spike sorting approach with neuromorphic models. J Neural Eng 2023; 20:056006. [PMID: 37659393 DOI: 10.1088/1741-2552/acf61d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 09/01/2023] [Indexed: 09/04/2023]
Abstract
Objective.Spike sorting, a critical step in neural data processing, aims to classify spiking events from single electrode recordings based on different waveforms. This study aims to develop a novel online spike sorter, NeuSort, using neuromorphic models, with the ability to adaptively adjust to changes in neural signals, including waveform deformations and the appearance of new neurons.Approach.NeuSort leverages a neuromorphic model to emulate template-matching processes. This model incorporates plasticity learning mechanisms inspired by biological neural systems, facilitating real-time adjustments to online parameters.Results.Experimental findings demonstrate NeuSort's ability to track neuron activities amidst waveform deformations and identify new neurons in real-time. NeuSort excels in handling non-stationary neural signals, significantly enhancing its applicability for long-term spike sorting tasks. Moreover, its implementation on neuromorphic chips guarantees ultra-low energy consumption during computation.Significance.NeuSort caters to the demand for real-time spike sorting in brain-machine interfaces through a neuromorphic approach. Its unsupervised, automated spike sorting process makes it a plug-and-play solution for online spike sorting.
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Affiliation(s)
- Hang Yu
- State Key Lab of Brain-Machine Intelligence, Hangzhou, People's Republic of China
- College of Computer Science and Technology, Zhejiang University, Hangzhou, People's Republic of China
| | - Yu Qi
- State Key Lab of Brain-Machine Intelligence, Hangzhou, People's Republic of China
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Hangzhou, People's Republic of China
- MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Gang Pan
- State Key Lab of Brain-Machine Intelligence, Hangzhou, People's Republic of China
- College of Computer Science and Technology, Zhejiang University, Hangzhou, People's Republic of China
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Edge computing on TPU for brain implant signal analysis. Neural Netw 2023; 162:212-224. [PMID: 36921432 DOI: 10.1016/j.neunet.2023.02.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 01/18/2023] [Accepted: 02/23/2023] [Indexed: 03/04/2023]
Abstract
The ever-increasing number of recording sites of silicon-based probes imposes a great challenge for detecting and evaluating single-unit activities in an accurate and efficient manner. Currently separate solutions are available for high precision offline evaluation and separate solutions for embedded systems where computational resources are more limited. We propose a deep learning-based spike sorting system, that utilizes both unsupervised and supervised paradigms to learn a general feature embedding space and detect neural activity in raw data as well as predict the feature vectors for sorting. The unsupervised component uses contrastive learning to extract features from individual waveforms, while the supervised component is based on the MobileNetV2 architecture. One of the key advantages of our system is that it can be trained on multiple, diverse datasets simultaneously, resulting in greater generalizability than previous deep learning-based models. We demonstrate that the proposed model does not only reaches the accuracy of current state-of-art offline spike sorting methods but has the unique potential to run on edge Tensor Processing Units (TPUs), specialized chips designed for artificial intelligence and edge computing. We compare our model performance with state of art solutions on paired datasets as well as on hybrid recordings as well. The herein demonstrated system paves the way to the integration of deep learning-based spike sorting algorithms into wearable electronic devices, which will be a crucial element of high-end brain-computer interfaces.
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Foroozmehr F, Nazari B, Sadri S, Rikhtehgaran R. Spike Sorting of Non-Stationary Data in Successive Intervals Based on Dirichlet Process Mixtures. Cogn Neurodyn 2022; 16:1393-1405. [PMID: 36408062 PMCID: PMC9666609 DOI: 10.1007/s11571-022-09781-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 12/10/2021] [Accepted: 01/08/2022] [Indexed: 11/30/2022] Open
Abstract
This paper proposes a new automatic method for spike sorting and tracking non-stationary data based on the Dirichlet Process Mixture (DPM). Data is divided into non-overlapping intervals and mixtures are applied to individual frames rather than to the whole data. In this paper, we have used the information of the previous frame to estimate the cluster parameters of the current interval. Specifically, the means of the clusters in the previous frame are used for estimating the cluster means of the current one, and other parameters are estimated via noninformative priors. The proposed method is capable to track variations in size, shape, or location of clusters as well as detecting the appearance and disappearance of them. We present results in two-dimensional space of first and second principal components (PC1-PC2), but any other feature extraction method leading to the ability of modeling spikes with Normal or t-Student distributions can also be applied. Application of this approach to simulated data and the recordings from anesthetized rat hippocampus confirms its superior performance in comparison to a standard DPM that uses no information from previous frames.
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Affiliation(s)
- Foozie Foroozmehr
- Department of Electrical and Computer Engineering, Isfahan University of Technology, 84156-83111 Isfahan, Iran
| | - Behzad Nazari
- Department of Electrical and Computer Engineering, Isfahan University of Technology, 84156-83111 Isfahan, Iran
| | - Saeed Sadri
- Department of Electrical and Computer Engineering, Isfahan University of Technology, 84156-83111 Isfahan, Iran
| | - Reyhaneh Rikhtehgaran
- Department of Mathematical Sciences, Isfahan University of Technology, 84156-83111 Isfahan, Iran
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A robust spike sorting method based on the joint optimization of linear discrimination analysis and density peaks. Sci Rep 2022; 12:15504. [PMID: 36109581 PMCID: PMC9477889 DOI: 10.1038/s41598-022-19771-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 09/05/2022] [Indexed: 11/08/2022] Open
Abstract
Spike sorting is a fundamental step in extracting single-unit activity from neural ensemble recordings, which play an important role in basic neuroscience and neurotechnologies. A few algorithms have been applied in spike sorting. However, when noise level or waveform similarity becomes relatively high, their robustness still faces a big challenge. In this study, we propose a spike sorting method combining Linear Discriminant Analysis (LDA) and Density Peaks (DP) for feature extraction and clustering. Relying on the joint optimization of LDA and DP: DP provides more accurate classification labels for LDA, LDA extracts more discriminative features to cluster for DP, and the algorithm achieves high performance after iteration. We first compared the proposed LDA-DP algorithm with several algorithms on one publicly available simulated dataset and one real rodent neural dataset with different noise levels. We further demonstrated the performance of the LDA-DP method on a real neural dataset from non-human primates with more complex distribution characteristics. The results show that our LDA-DP algorithm extracts a more discriminative feature subspace and achieves better cluster quality than previously established methods in both simulated and real data. Especially in the neural recordings with high noise levels or waveform similarity, the LDA-DP still yields a robust performance with automatic detection of the number of clusters. The proposed LDA-DP algorithm achieved high sorting accuracy and robustness to noise, which offers a promising tool for spike sorting and facilitates the following analysis of neural population activity.
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Bod RB, Rokai J, Meszéna D, Fiáth R, Ulbert I, Márton G. From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings. Front Neuroinform 2022; 16:851024. [PMID: 35769832 PMCID: PMC9236662 DOI: 10.3389/fninf.2022.851024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 05/06/2022] [Indexed: 11/15/2022] Open
Abstract
The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript.
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Affiliation(s)
- Réka Barbara Bod
- Laboratory of Experimental Neurophysiology, Department of Physiology, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Târgu Mureş, Târgu Mureş, Romania
| | - János Rokai
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- School of PhD Studies, Semmelweis University, Budapest, Hungary
| | - Domokos Meszéna
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Richárd Fiáth
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - István Ulbert
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Gergely Márton
- Integrative Neuroscience Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
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