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:  Stacking: 
1. Abstract of Cited Paper (BIB001):  Electroencephalogram (EEG) pattern classification plays an important role in the domain of brain computer interface (BCI). Hidden Markov model (HMM) might be a useful tool in EEG pattern classification since EEG data is a multivariate time series data which contains noise and artifacts. In this paper we present methods for EEG pattern classification which jointly employ principal component analysis (PCA) and HMM. Along this line, two methods are introduced: (1) PCA+HMM; (2) PCA+HMM+SVM. Usefulness of principal component features and our hybrid method is confirmed through the classification of EEG that is recorded during the imagination of a left or right hand movement.

Literature Review:
Stacking consists in using several classifiers, each of them classifying the input feature vector. These classifier are called level-0 classifiers. The output of each of these classifiers is then given as input to a so-called meta-classifier (or level-1 classifier) which makes the final decision BIB001 . Stacking has been used in BCI research using HMM as level-0 classifiers, and an SVM as meta-classifier BIB002 ; The main advantage of such techniques is that a combination of similar classifiers is very likely to outperform one of the classifiers on its own. Actually, combining classifiers is known to reduce the Variance (see Section 2.2.2) and thus the classification error  [28].