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:  Feature properties 
1. Abstract of Cited Paper (BIB001):  Presents an overview of the authors' research into brain-computer interfacing (BCI). This comprises an offline study of the effect of motor imagery on EEG and an online study that uses pattern classifiers incorporating parameter uncertainty and temporal information to discriminate between different cognitive tasks in real-time. 
2. Abstract of Cited Paper (BIB002):  With the increasing efficiency of life-support systems and better intensive care, more patients survive severe injuries of the brain and spinal cord. Many of these patients experience locked-in syndrome: The active mind is locked in a paralyzed body. Consequently, communication is extremely restricted or impossible. A muscle-independent communication channel overcomes this problem and is realized through a brain-computer interface, a direct connection between brain and computer. The number of technically elaborated brain-computer interfaces is in contrast with the number of systems used in the daily life of locked-in patients. It is hypothesized that a profound knowledge and consideration of psychological principles are necessary to make brain-computer interfaces feasible for locked-in patients. 
3. Abstract of Cited Paper (BIB003):  In this communication, we give an overview of our work on an asynchronous brain-computer interface (where the subject makes self-paced decisions on when to switch from one mental task to the next) that responds every 0.5 s. A local neural classifier tries to recognize three different mental tasks; it may also respond "unknown" for uncertain samples as the classifier has incorporated statistical rejection criteria. We report our experience with 15 subjects. We also briefly describe two brain-actuated applications we have developed: a virtual keyboard and a mobile robot (emulating a motorized wheelchair). 
4. Abstract of Cited Paper (BIB004):  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. 
5. Abstract of Cited Paper (BIB005):  Abstract Objective To develop a single trial motor imagery (MI) classification strategy for the brain–computer interface (BCI) applications by using time–frequency synthesis approach to accommodate the individual difference, and using the spatial patterns derived from electroencephalogram (EEG) rhythmic components as the feature description. Methods The EEGs are decomposed into a series of frequency bands, and the instantaneous power is represented by the envelop of oscillatory activity, which forms the spatial patterns for a given electrode montage at a time–frequency grid. Time–frequency weights determined by training process are used to synthesize the contributions from the time–frequency domains. Results The present method was tested in nine human subjects performing left or right hand movement imagery tasks. The overall classification accuracies for nine human subjects were about 80% in the 10-fold cross-validation, without rejecting any trials from the dataset. The loci of MI activity were shown in the spatial topography of differential-mode patterns over the sensorimotor area. Conclusions The present method does not contain a priori subject-dependent parameters, and is computationally efficient. The testing results are promising considering the fact that no trials are excluded due to noise or artifact. Significance The present method promises to provide a useful alternative as a general purpose classification procedure for MI classification. 
6. Abstract of Cited Paper (BIB006):  We report a pilot study of performing classification of motor imagery for brain–computer interface applications, by means of source analysis of scalp-recorded EEGs. Independent component analysis (ICA) was used as a spatio-temporal filter extracting signal components relevant to left or right motor imagery (MI) tasks. Source analysis methods including equivalent dipole analysis and cortical current density imaging were applied to reconstruct equivalent neural sources corresponding to MI, and classification was performed based on the inverse solutions. The classification was considered correct if the equivalent source was found over the motor cortex in the corresponding hemisphere. A classification rate of about 80% was achieved in the human subject studied using both the equivalent dipole analysis and the cortical current density imaging analysis. The present promising results suggest that the source analysis approach could manifest a clearer picture on the cortical activity, and thus facilitate the classification of MI tasks from scalp EEGs. 
7. Abstract of Cited Paper (BIB007):  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.

Literature Review:
A great variety of features have been attempted to design BCI such as amplitude values of EEG signals  , Band Powers (BP) [11] , Power Spectral Density (PSD) values BIB005 BIB004 , AutoRegressive (AR) and Adaptive AutoRegressive (AAR) parameters BIB002 BIB001 , Time-frequency features BIB006 and inverse model-based features BIB007 [17] [18] . Concerning the design of a BCI system, some critical properties of these features must be considered: • noise and outliers: BCI features are noisy or contain outliers because EEG signals have a poor signal-to-noise ratio; • high dimensionality: In BCI systems, feature vectors are often of high dimensionality, e.g., BIB008 . Indeed, several features are generally extracted from several channels and from several time segments before being concatenated into a single feature vector (see next section); • time information: BCI features should contain time information as brain activity patterns are generally related to specific time variations of EEG (see next section); inria-00134950, version 1 -6 Mar 2007 • non-stationarity: BCI features are non-stationary since EEG signals may rapidly vary over time and more especially over sessions; • small training sets: The training sets are relatively small, since the training process is time consuming and demanding for the subjects. These properties are verified for most features currently used in BCI research. However, it should be noted that it may no longer be true for BCI used in clinical practice. For instance, the training sets obtained for a given patient would not be small anymore as a huge quantity of data would have been acquired during sessions performed over days and months. As the use of BCI in clinical pratice is still very limited BIB003 , this paper deals with classification methods used in BCI research. However, the reader should be aware that problems may be different for BCI used outside the laboratories.