## 1 Introduction
They proposed a Markov sequence decision and constructed a dynamic reward function (DRF) to improve the sensitivity of the agent to minor classes based on the inter-class imbalance ratios. Another paper using deep learning was presented in [22], where the authors developed a deep learning method, which learns contextualized, temporal, and spatial patterns and classifies them as channels containing neural spike data or only noise. From this, they created a batch of waveforms to detect spikes in data recorded from a single tetraplegic patient. From the same author, in [14], a method based on K-means for classifying the detected spikes was presented. The methodology employed by these authors aligns closely with our approach, as we share a parallel trajectory. Much like their sequence, we initially published a paper focusing on spike detection and shifted our focus toward classifying the detected spikes. However, in our instance, our exploration delves into distinct clustering algorithms. The K-means algorithm was also recently investigated with template optimization in phase space as demonstrated in [8]. This method obtained interesting results on both simulated data and real neural recordings. Wavelet [23] is another approach widely used in the analysis of biosignals. In this paper, the authors use a continuous wavelet transform (CWT) with optimized parameters to sort artificial and real data. The authors suggest that their results outperform those based on PCA. Several notable and contemporary contributions within the realm of spike sorting have been highlighted [24]. Nevertheless, providing an exhaustive review of all existing literature in this domain is an intricate task, further compounded by the challenges of conducting a direct comparison across this large number of works. The intricacy arises due to the divergent employment of various databases and algorithms for detecting and classifying potentials. In this context, we have spotlighted articles resembling our proposition, particularly those grounded in PCA and K-means methodologies. However, to the best of our knowledge, our pursuit did not yield evidence of articles employing hierarchical clustering or Self-Organizing Maps as classification methods in neural recordings. Even those employing PCA and K-means do so in a manner distinct from the approach proposed in this study. Specifically, our approach involves an initial step of utilizing spike detections derived from a prior research endeavor conducted by the research group and documented in [25]. This antecedent work employed an adaptive threshold method for real-time action potential detection. This precursor dataset facilitated the derivation of feature matrices capturing statistical attributes of the action potentials. These matrices were then subjected to three distinct clustering algorithms: K-means, hierarchical clustering, and Self-Organizing Maps (SOMs). Notably, within the specific context of K-means, PCA was integrated to reduce dimensionality and enhance the overall outcomes. The article is structured as follows: First, the introduction is presented in Section 1. Subsequently, the research methodology developed in this article is outlined in Section 2. The biosignals and spike detection are covered in Section 2.1. Unsupervised classification methods and results are presented in Section 3. Finally, the discussion and the conclusions are drawn in Sections 4 and 5, respectively.