You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces 
Chapter Title:  MultiLayer Perceptron 
1. Abstract of Cited Paper (BIB001):  The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field. 
2. Abstract of Cited Paper (BIB002):  We compare the use of two Markovian models, HMMs and IOHMMs, to discriminate between three mental tasks for brain computer interface systems using an asynchronous protocol. We show that IOHMMs outperform HMMs but that, probably due to the lack of any prior information on the state dynamics, no practical advantage in the use of these models over their static counterparts is obtained. 
3. Abstract of Cited Paper (BIB003):  This paper presents an algorithm for classifying single-trial electroencephalogram (EEG) during the preparation of self-paced tapping. It combines common spatial subspace decomposition with Fisher discriminant analysis to extract features from multichannel EEG. Three features are obtained based on Bereitschaftspotential and event-related desynchronization. Finally, a perceptron neural network is trained as the classifier. This algorithm was applied to the data set of "BCI Competition 2003" with a classification accuracy of 84% on the test set. 
4. Abstract of Cited Paper (BIB004):  In this paper, a brain computer interface (BCI) is designed using electroencephalogram (EEG) signals where the subjects have to think of only a single mental task. The method uses spectral power and power difference in 4 bands: delta and theta, beta, alpha and gamma. This could be used as an alternative to the existing BCI designs that require classification of several mental tasks. In addition, an attempt is made to show that different subjects require different mental task for minimising the error in BCI output. In the experimental study, EEG signals were recorded from 4 subjects while they were thinking of 4 different mental tasks. Combinations of resting (baseline) state and another mental task are studied at a time for each subject. Spectral powers in the 4 bands from 6 channels are computed using the energy of the elliptic FIR filter output. The mental tasks are detected by a neural network classifier. The results show that classification accuracy up to 97.5% is possible, provided that the most suitable mental task is used. As an application, the proposed method could be used to move a cursor on the screen. If cursor movement is used with a translation scheme like Morse code, the subjects could use the proposed BCI for constructing letters/words. This would be very useful for paralysed individuals to communicate with their external surroundings 
5. Abstract of Cited Paper (BIB005):  Fast and simple classification methods for brain computer interfacing (BCI) signals are indispensable for the design of successful BCI applications. This paper presents a computationally simple algorithm to classify BCI data into left and right finger movements of the subjects. A two-class output multilayer perceptron (MLP) performs the classification. Our approach is attractive for providing an optimal combination of 1) computational efficiency, 2) classification accuracy (training: 100% and testing: 64%) and 3) minimal feature extraction (two channels out of a 28-channel EEG trial). The channels selected to be extracted (C3 and C4) not only greatly reduce dimensionality, but also refer to the central parts of the brain that decide left- right cognition, greatly enhancing the classification task. The results obtained are promising, and hold much potential for further investigation.

Literature Review:
An MLP is composed of several layers of neurons: an input layer, possibly one or several hidden layers, and an output layer  . Each neuron's input is connected with the output of the previous layer's neurons whereas the neurons of the output layer determine the class of the input feature vector. Neural Networks and thus MLP, are universal approximators, i.e., when composed of enough neurons and layers, they can approximate any continuous function. Added to the fact that they can classify any number of classes, this makes NN very flexible classifiers that can adapt to a great variety of problems. Consequently, MLP, which are the most popular NN used in classification, have been applied to almost all BCI problems such as binary BIB005 or multiclass BIB001 , synchronous  or asynchronous BIB003 BCI. However, the fact that MLP are universal approximators makes these classifiers sensitive to overtraining, especially with such noisy and non-stationary data as EEG, e.g., BIB006 . Therefore, careful architecture selection and regularization is required BIB002 . A MultiLayer Perceptron without hidden layers is known as a perceptron. Interestingly enough, a perceptron is equivalent to LDA and, as such, has been sometimes used for BCI applications [18] BIB004