## 4 Results
### Comparison of the Algorithms Used for Classifying Motor Imagery  
Here, we focus on identifying features and classification algorithms which identify the imagination of upper-limb movements with the best performance. According to the various studies surveyed, the classification performance ranges from 62% to 95%, with an average of 77% (Table 1). The majority of papers use CSP for extracting the MI features that will afterwards be classified, in most cases by LDA [16, 22, 77, 78, 80, 83]. The rest of the papers used either traditional machine-learning methods, such as SVM [21, 79], logistic regression (LR) [85], naive Bayes (NB) [86, 87], quadratic discriminant analysis (QDA) [76], and fuzzy logic systems (T2 FLS) [19, 75], or deep-learning techniques, such as convolutional neural networks (CNN) [81, 82, 84] and autoencoder [19]. Further, [PERSON] et al., 2017 [78] and [PERSON] et al., 2021 [21] reported the best classification accuracy at 95% and 93% utilizing LDA and SVM, respectively. In both cases, the feature-extraction method used was CSP. As illustrated in Figure 1a, there is no statistically significant correlation between the classification performance and time in years (\(r=0.3\), \(p=0.23\)). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline _Author_ & _Classifier_ & _Performance_ & _Feature Extraction_ & _Number of Electrodes_ & _Number of_ & _Number of_ & _Number of_ & _Feedback_ & _Participants_ & _Vars_ \\ \hline [PERSON] [75] & T2 FLS & 69\% & PSD & 2 & 6 & 7 & 160 & Screen & Healthy & - \\ [PERSON] [19] & T2 FLS & 69\% & PSD & 2 & 5 & 12 & 160 & Screen & Patients & 59 \\ [PERSON] [76] & QDA & 67\% & CSP+AR & 3 & 3 & 1 & 230 & Screen & Healthy & - \\ [PERSON] [53] & Autoencoder & 74\% & CSP & 16 & 4 & 144 & 15 & Screen+ FES & Patients & 62 \\ [PERSON] [77] & LDA & 86\% & WT+AR & 2 & 8 & 3 & 40 & Robotic & Healthy & 27 \\ [PERSON] [78] & LDA & 95\% & CSP & 45 & 2 & 10 & 240 & Screen+ FES & Patients & 50 \\ [PERSON] [79] & SVM & 74\% & CSP & 41 & 5 & 1 & 40 & Screen & Healthy & - \\ [PERSON] [80] & LDA & 87\% & CSP & 64 & 5 & 18 & 160 & Screen+ FES & Patients & 60 \\ [PERSON] [81] & CNN & 84\% & FT & 3 & 20 & 2 & 90 & Robotic & Healthy & 31 \\ [PERSON] [82] & CNN & 72\% & - & 16 & 10 & - & - & VR & Healthy & 25 \\ [PERSON] [83] & LDA & 82\% & CSP & 64 & 15 & 1 & 300 & Robotic & Healthy & - \\ [PERSON] [84] & CNN & 70\% & CSP & 12 & 10 & 1 & 120 & Robotic & Patients & 41 \\ [PERSON] [85] & LR & 62\% & CSP & 64 & 12 & 1 & 180 & Screen & Healthy & 20 \\ [PERSON] [86] & NB & 68\% & PLV & 64 & 16 & 2 & 50 & Screen & Patients & 50 \\ [PERSON] [21] & SVM & 93\% & CSP & 16 & 20 & - & - & VR-FES & Healthy & 26 \\ [PERSON] [22] & LDA & 80\% & CSP & 12 & 10 & 3 & 40 & Robotic & Patients & 41 \\ [PERSON] [87] & NB & 80\% & CSP & 30 & 11 & 6 & - & Screen & Healthy & 26 \\ [PERSON] [16] & LDA & 75\% & WT+AR & 16 & 7 & 3 & 200 & Screen & Patients & 60 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of papers included in the review. Apart from the various classification methods utilized in the BCIs, it is important to investigate the features that are extracted from the EEG signal and the neurofeedback that is provided to subjects. In an attempt to discover if there is a particular BCI configuration that gives rise to the best performance, we present the different feature-extraction methods and types of neurofeedback with respect to the classification accuracy (Figure 1b). With solid blue lines, we indicate the BCI approaches that exceed the average classification accuracy (77%), while dashed lines indicate the BCI configurations that performed with a classification accuracy below the average. In the literature, we discovered various feature-extraction techniques, such as spatial filters; CSP was the most used and provided the most distinguishable features. The other methods used are selecting the features either from the frequency domain, FT and power spectrum density (PSD), or the time-frequency domain, WT and AR.