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:  Combination of classifiers: 
1. Abstract of Cited Paper (BIB001):  High rate classification of imagery tasks is still one of the hot topics among the brain computer interface (BCI) groups. In order to improve this rate, a new approach based on fractal dimension as feature and Adaboost as classifier is presented for five subjects in this paper. To have a comparison, features such as band power, Hjorth parameters along with LDA classifier have been taken into account. Fractal dimension as a feature with Adaboost and LDA can be considered as alternative combinations for BCI applications. 
2. Abstract of Cited Paper (BIB002):  This paper addresses the problem of signal responses variability within a single subject in P300 speller Brain-Computer Interfaces. We propose here a method to cope with these variabilities by considering a single learner for each acquisition session. Each learner consists of a channel selection procedure and a classifier. Our algorithm has been benchmarked with the data and the results of the BCI 2003 competition dataset and we clearly show that our approach yields to state-of-the art results. 
3. Abstract of Cited Paper (BIB003):  Gradient boosting is a machine learning method, that builds one strong classifier from many weak classifiers. In this work, an algorithm based on gradient boosting is presented, that detects event-related potentials in single electroencephalogram (EEG) trials. The algorithm is used to detect the P300 in the human EEG and to build a brain-computer interface (BCI), specifically a spelling device. Important features of the method described here are its high classification accuracy and its conceptual simplicity. The algorithm was tested with datasets recorded in our lab and one benchmark dataset from the BCI Competition 2003. The number of correctly inferred symbols with the P300 speller paradigm varied between 90% and 100%. In particular, all of the inferred symbols were correct for the BCI competition dataset 
4. Abstract of Cited Paper (BIB004):  In recent years, brain-computer interface (BCI) technology has emerged very rapidly. Brain-computer interfaces (BCIs) bring us a new communication interface technology which can translate brain activities into control signals of devices like computers, robots. The preprocessing of electroencephalographic (EEG) signal and translation algorithms play an important role in EEG-based BCIs. In this study, we employed an independent component analysis (ICA)-based preprocessing method and a committee machine-based translation algorithm for the offline analysis of a cursor control experiment. The results show that ICA is an efficient preprocessing method and the committee machine is a good choice for translation algorithm.

Literature Review:
Combining classifiers was shown efficient BIB003 [72] BIB004 and almost always outperformed a single one BIB003 [72] BIB005 (see Table A3 ). Similarly, on data set IIb of BCI competition 2003, the best results, i.e., best accuracy and smallest number of repetitions, were obtained with combinations of classifiers, namely, Boosting of OLS BIB004 and Voting of SVM BIB003 (see Table A5 ). The study in BIB002 is an exception as a Boosting of MLP was outperformed by a single LDA. This may be explained by the sensitivity of boosting to mislabels BIB001 and the fact that these mislabels are likely to occur in such noisy and uncertain data as EEG signals. Therefore, combinations such as Voting or Stacking may be prefered for BCI applications. As seen in Section 3.5, the combination of classifiers helps reducing the Variance component of the classification error which generally makes combinations of classifiers more efficient than their single counterparts  . Furthermore, this Variance reflects the sensitivity towards the training set used. In BCI experiments, Variance can be due to time variability  [21], session-to-session variability BIB003 or subjectto-subject variability. Therefore, Variance probably is an important source of error. Combining classifiers may be a way of solving this variability/non-stationarity problem BIB003 , which may explain its success.