IEEE Trans Neural Syst Rehabil Eng. 2021;29:750-759. doi: 10.1109/TNSRE.2021.3074162. Epub 2021 Apr 26.
Spike sorting technologies support neuroscientists to access the neural activity with single-neuron or single-action-potential resolutions. However, conventional spike sorting technologies perform the feature extraction and the clustering separately after the spikes are well detected. It not only induces many redundant processes, but it also yields a lower accuracy and an unstable result especially when noises and/or overlapping spikes exist in the dataset. To address these issues, this paper proposes a unified optimization model integrating the feature extraction and the clustering for spike sorting. Unlike the widely used combination strategies, i.e., performing the principal component analysis (PCA) for spike feature extraction and the K-means (KM) for clustering in sequence, interestingly, this paper finds the solution of the proposed unified model by iteratively performing PCA and KM-like procedures. Subsequently, by embedding the K-means++ strategy in KM-like initializing and a comparison updating rule in the solving process, the proposed model can well handle the noises and overlapping interference as well as enjoy a high accuracy and a low computational complexity. Finally, an automatic spike sorting method is derived after taking the best of the clustering validity indices into the proposed model. The extensive numerical simulation results on both synthetic and real-world datasets confirm that our proposed method outperforms the related state-of-the-art approaches.
尖峰分类技术支持神经科学家以单神经元或单动作电位分辨率获取神经活动。然而，传统的尖峰分类技术在尖峰被很好地检测之后分别进行特征提取和聚类。这不仅引入了许多冗余的过程，而且在数据集中存在噪声和/或重叠尖峰时，精度和结果也不稳定。为了解决这些问题，本文提出了一种用于尖峰分类的统一优化模型，该模型将特征提取和聚类集成在一起。与广泛使用的组合策略不同，即依次进行主成分分析 (PCA) 进行尖峰特征提取和 K-均值 (KM) 聚类，本文有趣地通过迭代执行 PCA 和类似 KM 的过程找到了所提出的统一模型的解决方案。随后，通过在类似 KM 的初始化中嵌入 K-均值++策略以及在求解过程中比较更新规则，所提出的模型可以很好地处理噪声和重叠干扰，同时具有较高的准确性和较低的计算复杂度。最后，在将聚类有效性指标的最佳结果纳入所提出的模型后，得出了一种自动尖峰分类方法。对合成和真实数据集的广泛数值模拟结果证实，我们提出的方法优于相关的最先进方法。
