## 2 Literature Review
In [18], the BCI competition IV Dataset 2a was used, where nine subjects performed motor imagery on four movements: left hand, right hand, feet, and tongue. Feature extraction was performed using time-frequency CSP, while Linear Discriminant Analysis (LDA), naive Bayes, and Support Vector Machine (SVM) were used as classifiers. The results showed that the average computation time was 37.22% shorter than FBCSP, used by the first winner of BCI competition IV, and 4.98% longer than the conventional CSP method. [PERSON] and [PERSON] [19] used the WAY-EEG-GAL dataset for six-movement classification, involving HandStart, FirstDigitTouch, BothStartLoadPhase, LiftOff, Replace, and BothReleased. A random forest algorithm identified the important electrodes from the thirty-two available. Wavelet transform extracted features, which were inputs for the CNN classifier. This model achieved an accuracy rate of 93.22%. For a more comprehensive view of EEG classification techniques used in the past decade, consider other methods besides CSP. In [20], a public BCI Research database from NUST (National University of Sciences and Technology, Pakistan) was used. Eight channels (F3, Fz, C3, Cz, C4, Pz, O1, and O2) were selected. Classification of EEG to identify left and right arm movements was conducted using wavelet transformation (WT) and a multilayer perceptron neural network (MPNN or MLP), achieving 88.72% accuracy. [PERSON] et al. [21] created a publicly available dataset at physionet.org. Eight channels (FC3, FCz, FC4, C3, C1, CZ, C2, and C4) were used out of the sixty-four available. A band-pass filter from 0.5 Hz to 90 Hz and a notch filter to remove 50 Hz line noise were applied. Independent Component Analysis (ICA) filtered out artifacts. MATLAB's (V14.5) neural networks toolbox built a neural network (NN) with 1-20 hidden layers, and 'MySVM' software performed SVM classification. Classification accuracies were 89.8% using NN and 97.1% using SVM for left- and right-hand movements. In [22], EEG data were obtained from a 26-year-old male moving his right hand. Four channels (AF3, F7, F3, and FC5) were used. The EEG signals were pre-processed using a Butterworth band-pass filter (0.5-45 Hz). Feature extraction was performed using WT, Fast Fourier Transform (FFT), and Principal Component Analysis (PCA). Classification was achieved using MPNN to classify three movements: open arm, closed arm, and closed hand. The model, with one hidden layer, ten neurons, and 500 epochs, achieved classification performances of 91.1%, 86.7%, and 85.6% using the three feature extraction methods, respectively. The author of [23] created a custom dataset involving two subjects performing motor imagery of three different right-hand grasp movements. From twenty-four electrodes, the best combination was found using the Genetic Algorithm (GA), based on evolution and natural genetics. Preprocessing involved ICA and a Butterworth High Pass Filter with a 1 Hz cutoff frequency. Features included maximum mu-power, beta-power, and frequencies of maximum mu- and beta-power. Among the four NN classifiers, Probabilistic Neural Network (PNN) achieved the best classification accuracy at 61.96%. In [24], a public dataset from fifteen subjects performing six functional movements of a single limb (forearm pronation/supination, hand open/close, and elbow flexion/extension) was used. EEGLAB filtered out irrelevant channels, and ICA identified eye and muscle artifacts. A band-passfilter (8-30 Hz) was applied. PCA standardized and reduced data dimensionality. Wavelet Packet Decomposition (WPD) extracted features, providing multi-level time-frequency decomposition with good time localization. Classification was performed using a Wavelet Neural Network (WNN), typically comprising three layers: input, hidden, and output. This model achieved an intersubject classification accuracy of 86.27% for the six limb movements [24; 25].