## 6 Discussion  
If adequate imaging methods as MRI-guided and stereotactic CT-verified techniques are used in DBS surgery, no additional support tools need to be used [49]. However, the use of MERs is widely accepted during DBS surgery in order to validate the imaging methods and provide more information to the surgeon. In addition, MER signal analysis has proven to provide neuronal biomarkers to discriminate between the different brain structures located from dorsal to ventral STN borders. Recent studies detecting STN have employed feature selection methods for machine learning classifiers in order to increase the detection of the STN area [12], proving that the immediate information provided by the algorithm can support the medical team to improve clinical decision-making, together with surgeon criterion and imaging techniques [50]. In general, the results show that the proposed classification procedure, using the adequate features, can provide an accurate detection of the Subthalamic Area (STN-IN). The final set of 11 features mostly comprised those obtained from the Local Field Potential (LFP) using time, frequency, and higher-order statistics features. It is important to note that spike-dependent (SD) features are less relevant that spike-independent (SIN) features. This result indicates that Local Field Potential features provide enough information with which to discriminate between different brain structures, and the spike activity might depend on other factors rather than the brain structure (e.g., patient mental activity). This finding accords with previous results from STN recordings in PD [51]. Despite this general finding, further analysis should be made on spike activity as it might provide more information about the patient, not only related to the brain structure where the spike activity is located. High-order statistics are found to be the most discriminant feature group. Although the calculation of nonlinear frequency features is computationally expensive compared to the statistical temporal features, they must be used as long as the companion time does not exceed the feedback time (several seconds) given to the surgeon during the DBS implantation operation. In terms of clinical use, providing the surgical team with a real-time tool is a must for this type of algorithm as it is intended to be used online while the DBS operation is being carried out. Since the algorithm was developed using Matlab software, we developed a self-executable program ready to be run in the surgical room by a computer with access to MER acquisition channels. For the classification task, Machine Learning (ML) techniques have shown to be a lightweight model classification compared with Deep Learning (DL) techniques due to the requirement of resources and data availability. As a result, focusing on MER signal classification using ML to detect the STN anatomical boundaries has similarly been on the rise. In this work, we explore the ability of ML to train handcrafted features from MER signals regarding the STN region detection. Recent studies have shown the ability of handcraft feature selection methods to increase the detection of STN borders [12]. Several studies have proposed methods for STN \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Authors** & **\# Patients** & **\# Features** & **Classifier** & **Training** & **Accuracy (\%)** \\ \hline [PERSON] et al. [46] & 100 & 11 SD and SIN & Deep Neural network & 10-fold CV & 92.0 \\ \hline [PERSON]. et al. [47] & 26 & 1 SIN & Random Forest, SVM, and KNNs & LOOCV & 94.0 \\ \hline [PERSON] et al. [48] & 13 & 26 SD and SIN & Decision trees & LOOCV & 94.1 \\ \hline [PERSON] et al. [19] & 21 & 12 SD and SIN & Bagging, KNNs, SVM, Decision Tree, and AdaBoost & LOOCV & 94.8 \\ \hline Our work & 14 & 11 SD and SIN & SVM, KNNs, RF, DT, DA, and NN & LOOCV & 99.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._localization using deep learning methods [17; 20; 52].