## 1 Introduction
In the first group, spike-dependent features were chosen as the basis because they describe explicit information known as the firing rate; indexing firing; and potential field information being extracted from either time-domain or frequency-domain representations. However, methods operating upon spikes either amplitude or time spike segments are often susceptible to intra/inter variabilities induced from non-stationary MERs' signal nature and their environment acquisition, thereby leading to poor STN location classification and clinical outcomes [13]. In the second group, spike independence can be also used to classify non-stationary signals by extracting only relevant local field information features. They can be extracted in a number of ways, e.g., by dividing a signal into a number of segments and computing the features in each segment-based feature such as statistical or frequency information [14]. Spike-independent features employ time-frequency signal representation to explore the main MER activity hidden in the high- and low-frequency bands. Unfortunately, the use of such a method requires prior information of firing spike activity, and its relation to PD symptoms variations remains unknown in the cognitive neuroscience field [15]. These manual methods make it difficult to obtain a feature extraction method capable of capturing the deep characteristics needed to clearly point to the location of the STN region. For this reason, machine learning techniques have been effectively employed for the validation of the planned directions in the procedure of DBS surgery to obtain the correct position of electrodes inside the brain and determine the STN location. The wavelet method is used in [16]. Learning techniques such as linear discriminant analysis are used in [17]. K-nearest Neighbors (KNNs), Support Vector Machines (SVM) [18], and decision trees [19] are also used. But the most used techniques in the bibliography are Neural Networks (NNs) [20] and SVM [21]. In this paper, we show how a sparse representation of MER signals allows for the extraction of discriminant features, improving accuracy in the identification of STN during DBS surgery applied in patients with [PERSON]'s disease. We use spike-independent and spike-dependent features extracted from MER. The extraction and selection of the features can then be applied to machine learning classifiers. Thus, the main objective of this work is to assess the improvement of the classification performance when dealing with STN brain structure detection. A wide number of features and classification algorithms are described, achieving high accuracy in STN area detection. The remainder of this paper is organized as follows. Section 2 introduces the dataset, explains the proposed methodology, and details the used classification procedure: noise cancellation, baseline removal, and segmentation as preprocessing. Section 3 describes the feature extraction and selection. Section 4 outlines the classification procedure and parameters used to evaluate the performance of the classification. The results of the analysis and a discussion of these are presented in Sections 5 and 6, respectively. Finally, Section 7 concludes the paper.