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:  Guidelines to choose a classifier 
1. Abstract of Cited Paper (BIB001):  To determine and compare the performance of different classifiers applied to four-class EEG data is the goal of this communication. The EEG data were recorded with 60 electrodes from five subjects performing four different motor-imagery tasks. The EEG signal was modeled by an adaptive autoregressive (AAR) process whose parameters were extracted by Kalman filtering. By these AAR parameters four classifiers were obtained, namely minimum distance analysis (MDA)--for single-channel analysis, and linear discriminant analysis (LDA), k-nearest-neighbor (kNN) classifiers as well as support vector machine (SVM) classifiers for multi-channel analysis. The performance of all four classifiers was quantified and evaluated by Cohen's kappa coefficient, an advantageous measure we introduced here to BCI research for the first time. The single-channel results gave rise to topographic maps that revealed the channels with the highest level of separability between classes for each subject. Our results of the multi-channel analysis indicate SVM as the most successful classifier, whereas kNN performed worst.

Literature Review:
This section assesses the use of the algorithms presented in section 3. It aims at providing the readers with guidelines to help them choose a classifier adapted to a given context. The performances of the BCI using the classifiers described above are gathered in tables, in the appendix. Several measures of performance have been proposed in BCI, such as accuracy of classification, Kappa coefficient BIB002 , Mutual Information  , sensitivity and specificity BIB001 . The most common one is the accuracy of classification, i.e., the  percentage of correctly classified feature vectors. Consequently, this paper only considers this particular measure. Two different points of view are proposed. The first identifies the best classifier(s) for a given kind of BCI whereas the second identifies the best classifier(s) for a given kind of features.