This paper reviews classification algorithms used in EEG-based Brain-Computer Interfaces (BCIs). The authors summarize the most commonly used algorithms, their properties, and compare them in terms of performance. They provide guidelines for selecting the most suitable algorithm for a specific BCI application. BCIs allow users to communicate with electronic devices using brain activity, bypassing the need for peripheral muscle activity. Classification algorithms are essential for identifying brain activity patterns and translating them into commands. The paper discusses various classification approaches, including linear classifiers, neural networks, nonlinear Bayesian classifiers, nearest neighbor classifiers, and combinations of classifiers. Linear classifiers such as Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM) are widely used. SVMs are particularly effective due to their regularization properties, which help in handling noisy and high-dimensional data. LDA is also effective but may struggle with nonlinear data. Neural networks, including MultiLayer Perceptrons (MLPs), are used for their flexibility and ability to model complex patterns. However, they are sensitive to overtraining, especially with noisy EEG data. Other neural network architectures, such as Gaussian classifiers and Hidden Markov Models (HMMs), are also discussed. Nonlinear Bayesian classifiers, such as Bayes Quadratic and HMMs, are effective in handling complex data and can provide efficient rejection of uncertain samples. Nearest neighbor classifiers, like k-Nearest Neighbors (kNN), are simple but sensitive to the curse of dimensionality. Combination methods, such as boosting, voting, and stacking, are shown to be effective in reducing classification error by combining multiple classifiers. These methods are particularly useful in handling the variability and non-stationarity of EEG data. The paper concludes that SVMs are particularly efficient for synchronous BCI applications due to their regularization and immunity to the curse of dimensionality. Combinations of classifiers and dynamic classifiers also show promise. For asynchronous BCI, no single classifier is clearly superior, and dynamic classifiers may not perform as well due to the difficulty in identifying the start of mental tasks. The authors emphasize the importance of considering the specific characteristics of the BCI application and the features used when selecting a classifier. They also highlight the need for further research to explore new classification techniques and to address the challenges of real-world BCI applications.This paper reviews classification algorithms used in EEG-based Brain-Computer Interfaces (BCIs). The authors summarize the most commonly used algorithms, their properties, and compare them in terms of performance. They provide guidelines for selecting the most suitable algorithm for a specific BCI application. BCIs allow users to communicate with electronic devices using brain activity, bypassing the need for peripheral muscle activity. Classification algorithms are essential for identifying brain activity patterns and translating them into commands. The paper discusses various classification approaches, including linear classifiers, neural networks, nonlinear Bayesian classifiers, nearest neighbor classifiers, and combinations of classifiers. Linear classifiers such as Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM) are widely used. SVMs are particularly effective due to their regularization properties, which help in handling noisy and high-dimensional data. LDA is also effective but may struggle with nonlinear data. Neural networks, including MultiLayer Perceptrons (MLPs), are used for their flexibility and ability to model complex patterns. However, they are sensitive to overtraining, especially with noisy EEG data. Other neural network architectures, such as Gaussian classifiers and Hidden Markov Models (HMMs), are also discussed. Nonlinear Bayesian classifiers, such as Bayes Quadratic and HMMs, are effective in handling complex data and can provide efficient rejection of uncertain samples. Nearest neighbor classifiers, like k-Nearest Neighbors (kNN), are simple but sensitive to the curse of dimensionality. Combination methods, such as boosting, voting, and stacking, are shown to be effective in reducing classification error by combining multiple classifiers. These methods are particularly useful in handling the variability and non-stationarity of EEG data. The paper concludes that SVMs are particularly efficient for synchronous BCI applications due to their regularization and immunity to the curse of dimensionality. Combinations of classifiers and dynamic classifiers also show promise. For asynchronous BCI, no single classifier is clearly superior, and dynamic classifiers may not perform as well due to the difficulty in identifying the start of mental tasks. The authors emphasize the importance of considering the specific characteristics of the BCI application and the features used when selecting a classifier. They also highlight the need for further research to explore new classification techniques and to address the challenges of real-world BCI applications.