## 2 Motor Imagery Classification Pipelines
### Classification  
After feature extraction, the signals are classified into various classes of movement imagination (e.g., left or right MI) through the use of classifiers. A classifier is a model that is used to predict the class to which the feature belongs. There are two main types of classifiers: supervised and unsupervised [51]. Supervised classifiers are trained on labeled data, where the correct class for each feature is known. Examples of supervised classifiers include logistic regression, decision trees, and support vector machines (SVM). Unsupervised classifiers, on the other hand, are trained on data that is not labeled with the correct class. Examples of unsupervised classifiers include k-means clustering and Gaussian mixture models. In general, supervised classifiers tend to perform better than unsupervised classifiers because they are trained on more informative data [52]; hence, they are the most used classifier type in BCIs.  
Most of the reviews in the literature classified the classification methods into linear classifiers, non-linear classifiers, neural networks and deep neural networks [13; 15; 37]. The two main types of linear classifiers are linear discriminant analysis (LDA) [53; 54; 55; 56; 57; 58] and support vector machine (SVM) [21; 59; 60]. LDA has low computational requirements; however, it might provide poor results on complex non-linear EEG data [37]. SVM overcomes this obstacle by using non-linear kernel functions to map the data onto higher dimensional space [36]. Non-linear classifiers, on the other hand, are not as widespread and popular as linear classifiers and neural networks [61; 62]. Although k-nearest neighbor (k-NN) [63] and Bayesian classifiers [64] are generative and easy to implement, they are sensitive to irrelevant and redundant features [36]. Finally, fuzzy classification is another approach used for EEG classification, since EEG classification is a decision-making problem suited for fuzzy logic [40].  
An important upside of artificial neural networks (ANN) is that they take into account the dynamic nature of an EEG signal. They are assemblies of artificial neurons, arranged in layers, which can be used to approximate any nonlinear decision boundary. The multi-layer perceptron (MLP) and the Gaussian classifier are the most used neural-network architectures used in BCI research [65; 66]. In contrast to traditional neural networks, where weights have to be chosen carefully, deep-learning approaches, such as convolutional neural networks (CNN) [67; 68] and recurrent neural networks (RNN) [69; 70], have high descriptive power.  
Further, deep-learning algorithms, particularly CNNs, have been successful at performing feature extraction on EEG data. This allows the network to automatically learn features from the raw data that are relevant for a given task, such as classification [71]. However, it should be noted that CNNs were adopted in EEG signal processing after first being established as a tool in image processing. Therefore, when using CNNs for the classification of MI EEG, pre-processing of the input data might be needed. Either raw data is fed into the CNN, and the first layers of the network are devoted to extracting spatial and temporal information, or a time-frequency domain image is obtained from the data using STFT or WT [72; 73].  
Another way to use deep learning for feature extraction on EEG data is to use RNN, such as a long short-term memory (LSTM) networks [74]. LSTMs are able to capture temporal dependencies in the data by processing the data sequentially in time and using the hidden state to \"remember\" information from previous time points.  
Finally, additional deep-learning approaches include the use of restricted Boltzmann machines (RBMs), deep belief network (DBN), generative adversarial networks (GAN), and the variational autoencoder (VAE) [26].