# Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation
## 3 Hardware Implementation and Computational Method
### Computation of the RT-EPI Algorithm
#### 3.4.1 Algorithm Description
Thus, note that the Bern-Barcelona dataset is the logical selection when facing just the F-NF problem, provided its longer signal length (20 s) and its specialization in inter-ictal signals (recordings of seizure activity and three hours after the last seizure activity are excluded). Table 5 compares our results with previous works for the task of classifying the focal and non-focal signals using the Bern-Barcelona EEG dataset. It details the obtained classification accuracy, summarizes the extracted features, and indicates the type of classifier used in each work. Note that the purpose of this work was not to beat the accuracy results of previous works in the bibliography; our goal was to obtain, at the same time, the best classification procedure having the lowest possible computational load (for feature extraction and classification), aiming at its real-time implementation. Despite this, we obtained pretty good accuracy results. The 95.5% classification accuracy obtained in this work surpasses many other related works [13; 15; 16; 17; 18; 20; 21; 47], while some other works [19; 23; 24; 48] surpasses this result by a maximum of 1.5% classification accuracy ([19] achieved 97% classification accuracy). Thus, when not considering [25], the comparison with the other related works can be considered pretty good in light of the great simplification achieved for the feature extraction process. In turn, [PERSON] et al. [25] obtained to 98.6% classification accuracy (3.1% more than this work), but at the expense of using a computationally intensive tool, a deep neural network, which is far away from the simplicity sought in this work; that is the reason to exclude this work from comparison from this point forward. As it can be seen in Table 5, extracted features were used to proceed from computationally intensive processes, such as the decomposition of EEG signals using empirical mode decomposition (EMD) to extract intrinsic mode functions (IMFs). Thus, [PERSON] et al. [17] obtained 87.0% classification accuracy using five entropy features extracted from IMFs; [PERSON] et al. [18] also used entropy-based features from the EMD, DWT (discrete wavelet transform) and EMD-DWT domains, achieving 89.4% classification accuracy; and [PERSON] et al. [19] arrived to a 97% classification accuracy using features derived from Euclidean measures obtained from the phase space reconstructions (PSRs) of several IMFs obtained using EMD. The wavelet transform has been also a computationally intensive process used in related works as the basis of the feature extraction process. Thus, [PERSON] et al. [16] obtained several entropy features from the tunable-Q wavelet transform (TQWT), reporting a 95.0% classification accuracy; [PERSON] et al. [20] obtained 90.0% classification accuracy using as features, projections of the reconstructed phase space (RPS) from the rhythm separation achieved using the empirical wavelet transform (EWT); [PERSON] et al. [13] obtained 94.25% classification accuracy from various wavelet based entropies; [PERSON] et al. [15] obtained 84.67% classification accuracy using TQWT based multivariate sub-band fuzzy entropy with LS-SVM classifiers; and [PERSON] et al. [24] obtained 96.0% classification accuracy based on a set of features extracted from the dual tree complex wavelet transform (DT-CWT) and using an adaptive neuron fuzzy interference system (ANFIS). Other works use variational mode decomposition as the basis of feature extraction, such as [PERSON] et al. [47], who obtained a 95.2% classification accuracy using features such as refined composite multi scale dispersion entropy (RCMSDE), refined composite multiscale fuzzy entropy (RCMSFE) and autoregressive model (AR) coefficients extracted from variational mode decomposition (VMD), DWT and VMD-DWT domains; or [PERSON] et al. [23], who obtained 96.0% classification accuracy using spectral moment based features extracted from the modes of the clustering VMD (CVMD) and extreme learning machine classifiers.