HASS: High Accuracy Spike Sorting with Wavelet Package Decomposition and Mutual Information

Chen, Yao; Huang, Libo; He, Jiong; Zhao, Kunyao; Cai, Ruichu; Zhang, Hao

doi:10.1109/bibm.2018.8621401

Cited by 6 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These three spikes shape needed to be applied to the 300 spikes where the noise ratio was modified from 0.1 to 0.5 with 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45 in between. In each time the alignment was performed to recognize the spikes from the background noise by applying the thresholding to filter the spikes from the noise as shown in figure (5). The process starts with generating information from the dataset and then the number of spikes is selected (here 300).…”

Section: A Template Matching Methodsmentioning

confidence: 99%

“…In this work, we used two different intelligent modeling methods. The flowchart of the system is illustrated in figure (5). First, the information was obtained from the dataset and the number of spikes to be used for the training were selected.…”

Section: System Modelmentioning

confidence: 99%

“…The feature of the nerve cell near the recording site has demonstrated that not only the direction and distance, but also the intrinsic of the recording electrodes influence the shape of the action potential (Noce, Ciancio, & Zollol, 2018). This information helps us understand the inner mechanism and the nervous system implementation (Chen et al, 2019). The growth of microfabrication technology leads to large-scale electrode arrays to record the neuron signals in many channels simultaneously (Rey, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…However, when they applied White Gaussian (WG) for the feature extraction and SVM, the accuracy becam higher for the easy and difficult noise (99%). While authors (Chen et al, 2019) applied accurate supervised spike sorting based on the wavelet by compering two methods wavelet package decomposition on (WPD) and PCA. The clustering accuracy for the first methods reached 99.7% while with PCA reached 22.35%.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improving the Accuracy of Neurons Spike Sorting by Using Supervised Machine Learning

Ahmed Hussein,

Khorsheed Mohammed

2024

ACAD J NAWROZ UNIV

View full text Add to dashboard Cite

The brain is important for both the functioning and reasoning ability of the body. It plays a fundamental role in the coordination of body functioning as well as reasoning and thinking. To understand how the brain is working, we need to know how neurons communicate with each other by firing (Action potential) which is known as spike. To record these activities neurologists used the multi-electrode which record thousands of spikes at the same time. Therefore, neurologists used the Spike Sorting Algorithm (SSA) to know which spike belongs to which neuron. The accuracy of the spike sorting is the most important point. Accordingly, machine learning is used to improve the accuracy of the spike sorting. In this paper, the Principal Component Analysis (PCA) is implemented to extract features and for clustering step, the Supervised Machine Learning is applied by using the Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) to compare the accuracy of the supervised clustering with the Template Matching method. A comparison between the results of Applied Machine Learning is achieved at different levels of noise to check the accuracy of each algorithm. The results showed that when the noise level was low, KNN accuracy reached 100% while SVM reached 95% and template 100 %. However, when the noise level increased to 0.5, the accuracy of KNN became 94 % and template 85.6 % and SVM 90 %.

show abstract

Section: A Template Matching Methodsmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving the Accuracy of Neurons Spike Sorting by Using Supervised Machine Learning

Ahmed Hussein,

Khorsheed Mohammed

2024

ACAD J NAWROZ UNIV

View full text Add to dashboard Cite

show abstract

“…They could fall into two categories, i.e., the two-stage methods (performing the feature extraction and the clustering in turn) and the one-stage methods (performing both the feature extraction and the clustering simultaneously in a unified framework) [18], [19]. On the one hand, the two-stage methods include Wave clus [20], [21] and High-Accuracy Spike Sorting (HASS) [22], [9], which extract features in a time-frequency domain. There also exist methods that extract features only in the time domain, including the features from principal component analysis [23], linear discriminant analysis [24], locality preserving projection [25], etc.…”

Section: Introductionmentioning

confidence: 99%

Automatical Spike Sorting With Low-Rank and Sparse Representation

Huang,

Gan,

Zeng

et al. 2024

IEEE Trans. Biomed. Eng.

Self Cite

View full text Add to dashboard Cite

Spike sorting 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.

show abstract

Wavelets behind the scenes: Practical aspects, insights, and perspectives

Guido

2022

Physics Reports

View full text Add to dashboard Cite

HASS: High Accuracy Spike Sorting with Wavelet Package Decomposition and Mutual Information

Cited by 6 publications

References 13 publications

Improving the Accuracy of Neurons Spike Sorting by Using Supervised Machine Learning

Improving the Accuracy of Neurons Spike Sorting by Using Supervised Machine Learning

Automatical Spike Sorting With Low-Rank and Sparse Representation

Wavelets behind the scenes: Practical aspects, insights, and perspectives

Contact Info

Product

Resources

About