# Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation
## 1 Introduction
As with other pathologies [10; 11; 12], machine learning has been applied in epilepsy at many works [13; 14; 15] to classify EEG signals as normal versus epileptic or seizure versus inter-ictal. However, the most challenging classification problem is focal (F) versus non-focal (NF). The classification of seizure from normal and seizure-free signals has achieved a 100% classification accuracy. However, this goal has not been achieved to date for the classification of focal and non-focal EEG signals. Neurosorgeons have difficulties determining the brain region responsible of the initial seizure discharge, so this kind of classification may serve as a tool to help epileptologists to reset the epileptogenic area. Compared with signals of the epileptogenic areas, the signals from non-epileptic areas are more nonlinear, less random and more nonstationary. Many machine learning systems have been developed to classify and detect the epileptogenic source signals. [PERSON] et al. [13] used entropies derived from the coefficients of the wavelet transform of the EEG signals to feed a least squares-support vector machine (LS-SVM) model to distinguish focal and non-focal EEG signals. In [16], [PERSON] et al. also used the LS-SVM classifier to feed the entropies derived from some subbands decomposed using tunable-Q wavelet transform (TQWT). In [17], [PERSON] et al. utilized empirical mode decomposition (EMD) and entropy for the classification of focal and non-focal EEG signals. In this work, intrinsic mode functions (IMFs) from focal and non-focal EEG signals were extracted using EMD, and then the entropies were fed the input of a LS-SVM classifier. [PERSON] et al. [18] also used entropy-based features from the EMD, DWT (discrete wavelet transform) and EMD-DWT domains, along with a k-nearest neighbor (k-NN) classifier model. In turn, [PERSON] et al. [19] used features derived from euclidean measures obtained from the phase space reconstruction (PSR) of several IMFs, obtained using EMD as well. [PERSON] et al. [20] proposed the decomposition of the EEG signal into rhythms using the empirical wavelet transform (EWT), and then used some area measures from them as input for a LS-SVM classifier model, to recognize focal and non-focal EEG signals. Another work of [PERSON] et al. [15] also used multivariate subband entropy measures from TQWT along with multivariate fuzzy entropy in combination with a LS-SVM classifier model. [PERSON] et al. [21] also used SVM and k-NN classifiers fed by multifractal, detrended fluctuation analysis (MFDFA) based feature sets. [PERSON] et al. [22] used features derived from DFT-based rhythms of the EEG to fed the LS-SVM classifier. [PERSON] et al. [23] proposed the use of spectral moment based features extracted from the modes of the clustering VMD (CVMD) and extreme learning machine (ELM) classifiers. [PERSON] et al. [24] utilized features extracted from the dual tree complex wavelet transform (DT-CWT) to fed an adaptive neurons fuzzy interference system (ANFIS). However, these works tend to require a considerable computational load, especially the most recent ones. As an example, [PERSON] et al. [25] proposed a deep neural network (DNN) made up of two convolutional layers for feature extraction and three fully connected layers for classification. In this work, authors increased the classification accuracy a little at the expense of increasing, considerably, its computational needs. In turn, [PERSON] et al. [26] used both a deep convolutional autoencoder and an unsupervised learning scheme merging a deep convolutional variational autoencoder and a K-means algorithm. This progressive increase of the computational demand could impede the jump of using the epileptogenic source localization during surgery. This application needs a real-time implementation, and could be used as a help-decision tool by neurosurgeons to refine the localization of an epileptogenic area during resective epilepsy surgery. Note that the recent technology is mature enough to implement machine learning processes in real-time [27; 28].