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


  • Helat Ahmed Hussein Department of Computer Science, College of Science, University of Duhok, Duhok, KRG – Iraq
  • Ahmed Khorsheed Mohammed Department of Electrical and Computer Engineering, College of Engineering, University of Duhok, Duhok, KRG – Iraq



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 %.


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Abdulqader, D. M., Abdulazeez, A. M., & Zeebaree, D. Q. (2020). Machine learning supervised algorithms of gene selection: A review. Technology Reports of Kansai University, 62(3), 233–244.

Bˆ, H., & Mures, R. C. (2020). Machine Learning-Assisted Detection of Action Potentials in Extracellular Multi-Unit Recordings. IEEE, 16–25.

Bod, R. B., Rokai, J., Meszéna, D., Fiáth, R., Ulbert, I., & Márton, G. (2022). From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings. Frontiers in Neuroinformatics, 16(June).

Chaure, F. J., Rey, H. G., & Quian Quiroga, R. (2018). A novel and fully automatic spike-sorting implementation with variable number of features. Journal of Neurophysiology, 120(4), 1859–1871.

Chen, Y., Huang, L., He, J., Zhao, K., Cai, R., & Hao, Z. (2019). HASS: High Accuracy Spike Sorting with Wavelet Package Decomposition and Mutual Information. Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, 831–838.

Hilgen, G., Sorbaro, M., Pirmoradian, S., Muthmann, J. O., Kepiro, I. E., Ullo, S., Ramirez, C. J., Puente Encinas, A., Maccione, A., Berdondini, L., Murino, V., Sona, D., Cella Zanacchi, F., Sernagor, E., & Hennig, M. H. (2017). Unsupervised Spike Sorting for Large-Scale, High-Density Multielectrode Arrays. Cell Reports, 18(10), 2521–2532.

Kuang, Q., & Zhao, L. (2009). A practical GPU based kNN algorithm. International Symposium on Computer Science and Computational Technology (ISCSCT), 7(3), 151–155.

Mokri, Y., Salazar, R. F., Goodell, B., Baker, J., Gray, C. M., & Yen, S. C. (2017). Sorting overlapping spike waveforms from electrode and tetrode recordings. Frontiers in Neuroinformatics, 11(August), 1–15. Https://


Nath, V., & Levinson, S. E. (2014). Machine learning. In SpringerBriefs in Computer Science (Issue 9783319056050).

Noce, E., Ciancio, A. L., & Zollo, L. (2018). Spike detection: The first step towards an ENG-based neuroprosheses. Journal of Neuroscience Methods, 308, 294–308.

Noce, E., Ciancio, A. L., & Zollol, L. (2018). Accuracy Optimization of the Spike Sorting Algorithm for Classification of Neural Signals. Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, 2018-Augus, 131–135.

Quian Quiroga, R., & Nadasdy, Z. (2004). Communicated by Maneesh Sahani Unsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic Clustering. 1687, 1661–1687.

Rey, H. G., Pedreira, C., & Quian Quiroga, R. (2015). Past, present and future of spike sorting techniques. Brain Research Bulletin, 119, 106–117.

Sukiban, J., Voges, N., Dembek, T. A., Pauli, R., Visser-Vandewalle, V., Denker, M., Weber, I., Timmermann, L., & Grün, S. (2019). Evaluation of Spike Sorting Algorithms: Application to Human Subthalamic Nucleus Recordings and Simulations. Neuroscience, 414, 168–185.

Takekawa, T., Isomura, Y., & Fukai, T. (2012). Spike sorting of heterogeneous neuron types by multimodality-weighted PCA and explicit robust variational bayes. Frontiers in Neuroinformatics, 6(MARCH), 1–13.

Williams, I., Luan, S., Jackson, A., & Constandinou, T. G. (2015). A Scalable 32 Channel Neural Recording and Real-Time FPGA Based Spike Sorting System. IEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings, 16(8), 0–4.

Wu, T., Zhao, W., Guo, H., Lim, H. H., & Yang, Z. (2017). A Streaming PCA VLSI Chip for Neural Data Compression. IEEE Transactions on Biomedical Circuits and Systems, 11(6), 1290–1302.

Yang, K., Wu, H., & Zeng, Y. (2017). A Simple Deep Learning Method for Neuronal Spike Sorting. Journal of Physics: Conference Series, 910(1).

Zamani, M., Sokolic, J., Jiang, D., Renna, F., Rodrigues, M., & Demosthenous, A. (2020). Accurate, Very Low Computational Complexity Spike Sorting Using Unsupervised Matched Subspace Learning. IEEE Transactions on Biomedical Circuits and Systems, 14(2), 221–231.



How to Cite

Ahmed Hussein , H. ., & Khorsheed Mohammed , A. . (2024). Improving the Accuracy of Neurons Spike Sorting by Using Supervised Machine Learning. Academic Journal of Nawroz University, 13(1), 451–464.




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