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: A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals 
Chapter Title:  Results 
1. Abstract of Cited Paper (BIB001):  Abstract EEGs of 6 normal subjects were recorded during sequences of periodic left or right hand movement. Left or right was indicated by a visual cue. The question posed was: ‘Is it possible to move a cursor on a monitor to the right or left side using the EEG signals for cursor control?’ For this purpose the EEG during performance of hand movement was analyzed and classified on-line. A neural network in form of a learning vector quantizertion (LVQ) with an input dimension of 16 was trained to classify EEG patterns from two electrodes and two time windows. After two training sessions on 2 different days, 4 subjects showed a classification accuracy of 89–100%. For two subjects classification was not possible. These results show that in general movement specific EEG-patterns can be found, classified in real time and used to move a cursor on a monitor to the left or right. On-line EEG classification is necessary when the EEG is used as input signal to a brain computer interface (BCI). Such a BCI can be a help for handicapped people. 
2. Abstract of Cited Paper (BIB002):  In a gas insulated high voltage cable having an outer tubular, corrugated conductor and an inner centrally disposed conductor, a spacer construction for holding and spacing the inner conductor in the tubular outer conductor, wherein each spacer has at least two spacer elements of semi annular construction each and complementing each other for establishing a closed annulus, each element constructed for laterial slipping onto the inner conductor, and the two spacer elements are secured to each other to establish the closed annulus in positive disposition on the inner conductor. Additional features impede local voltage breakdown, positive interlock of parts and resilient load relief. 
3. Abstract of Cited Paper (BIB003):  When people learn to control the amplitudes of certain electroencephalogram (EEG) components (e.g., the 8-12 Hz /spl mu/-rhythm over sensorimotor cortex) and use them to move a cursor to a target on a video screen, feedback about performance is normally provided by cursor movement and by trial outcome (i.e., success or failure). The authors assessed the short-term effects of this feedback on EEG control. After subjects received initial training with feedback present, feedback was removed intermittently for periods of several minutes. Subjects still displayed EEG control when feedback was removed. Removal of cursor movement alone appeared to have effects comparable to removal of both cursor movement and trial outcome. These results show that, in the short-term at least, /spl mu/-rhythm control is not dependent on the sensory input provided by cursor movement. They also suggest that feedback can have inhibitory as well as facilitatory effects on EEG control, and that these effects vary across subjects. This finding has implications for the design of training procedures. 
4. Abstract of Cited Paper (BIB004):  Intention of movement of left or right index finger, or right foot is recognized in electroencephalograms (EEGs) from three subjects. The authors present a multichannel classification method that uses a "committee" of artificial neural networks to do this. The classification method automatically finds spatial regions on the skull relevant for the classification task. Depending on subject, correct recognition of intended movement was achieved in 75%-98% of trials not seen previously by the committee, on the basis of single EEGs of one-second duration. Frequency filtering did not improve recognition. Classification was optimal during the actual movement, but a first peak in the classification success rate was observed in all subjects already when they had been cued which movement later to perform. 
5. Abstract of Cited Paper (BIB005):  Motor imagery can modify the neuronal activity in the primary sensorimotor areas in a very similar way as observable with a real executed movement. One part of EEG-based brain-computer interfaces (BCI) is based on the recording and classification of circumscribed and transient EEG changes during different types of motor imagery such as, e.g., imagination of left-hand, right-hand, or foot movement. Features such as, e.g., band power or adaptive autoregressive parameters are either extracted in bipolar EEG recordings overlaying sensorimotor areas or from an array of electrodes located over central and neighboring areas. For the classification of the features, linear discrimination analysis and neural networks are used. Characteristic for the Graz BCI is that a classifier is set up in a learning session and updated after one or more sessions with online feedback using the procedure of "rapid prototyping." As a result, a discrimination of two brain states (e.g., leftversus right-hand movement imagination) can be reached within only a few days of training. At this time, a tetraplegic patient is able to operate an EEG-based control of a hand orthosis with nearly 100% classification accuracy by mental imagination of specific motor commands. 
6. Abstract of Cited Paper (BIB006):  A "virtual keyboard" (VK) is a letter spelling device operated for example by spontaneous electroencephalogram (EEG), whereby the EEC is modulated by mental hand and leg motor imagery. We report on three able-bodied subjects, operating the VK. The ability in the use of the VK varies between 0.85 and 0.5 letters/min in error-free writing. 
7. Abstract of Cited Paper (BIB007):  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). 
8. Abstract of Cited Paper (BIB008):  We report our investigation of classification of imagined left and right hand movements by applying source analysis methods. Independent component analysis (ICA) is used as a spatio-temporal filter, then equivalent dipole analysis and cortical current density imaging methods are applied to reconstruct equivalent sources, to aid classification of motor imagery tasks in a human subject. 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. 
9. Abstract of Cited Paper (BIB009):  Abstract Single-trial motor imagery classification is an integral part of a number of brain–computer interface (BCI) systems. The possible significance of the kind of imagery, involving rather kinesthetic or visual representations of actions, was addressed using the following experimental conditions: kinesthetic motor imagery (MIK), visual–motor imagery (MIV), motor execution (ME) and observation of movement (OOM). Based on multi-channel EEG recordings in 14 right-handed participants, we applied a learning classifier, the distinction sensitive learning vector quantization (DSLVQ) to identify relevant features (i.e., frequency bands, electrode sites) for recognition of the respective mental states. For ME and OOM, the overall classification accuracies were about 80%. The rates obtained for MIK (67%) were better than the results of MIV (56%). Moreover, the focus of activity during kinesthetic imagery was found close to the sensorimotor hand area, whereas visual–motor imagery did not reveal a clear spatial pattern. Consequently, to improve motor-imagery-based BCI control, user training should emphasize kinesthetic experiences instead of visual representations of actions.

Literature Review:
The detailed classification results of the survey are summarized in tables 3-8 (refer to supplementary data at stacks.iop.org/JNE/4/R32 for more detailed versions of tables 5-7). As mentioned in section 3, these six tables address the signal enhancement, feature selection/dimensionality reduction, feature extraction, feature classification and postprocessing methods used. The references listed for each subclass entry represent all the papers that reported on designs related to that sub-class. As such, one can find all the designs that have specific attributes of interest. For example, if one is interested in all BCI technology designs that have used parametric modeling (and specifically extracted AR parameters of the signal) to detect the sensorimotor activity, then all the references to the relevant papers can be found in table 5 under sensorimotor activity-parametric modeling (AR, AAR and ARX parameters). Alternatively, if one is looking for designs that do not have a feature extraction block but directly apply the support vector machine (SVM) classification method on the brain signal, then these papers can be easily located in the none-SVM class in supplementary data at stacks.iop.org/JNE/4/R32. Similarly, the designs that use the SVM classification method, regardless of the feature extraction technique used, are categorized under the SVM class in table 6. To enhance the clarity of tables 3-8, the following notations are used: (a) BCI designs based on multiple neuromechanisms (as defined in table 2) are presented in separate categories such as MN: sensorimotor activity + response to mental tasks, which show that the design is based on the sensorimotor activity and the response to mental tasks neuromechanisms. (b) Two or more methods that are consecutively used in a design block are separated by '−'. As an example, CSPlog transformation denotes a design that first applies common spatial patterns (CSP) on the signal and then applies a logarithmic function on the resulting timeseries. (c) Two methods that are applied simultaneously in a design component are separated by '+'. For example, AR parameters + PSD parameters corresponds to a design that uses both autoregressive (AR) and power-spectraldensity (PSD) features in the feature extraction block. (d) LRP + {CSP − log-transformation} denotes designs that use the two kinds of feature extraction methods separated by '+'. The first method is based on the extraction of lateralized readiness potentials (LRP), and the second feature extraction method is based on consecutively applying CSP followed by a logarithm function on the signals that are grouped in '{ }'. (e) To facilitate readability, we have provided an index of terms in appendix A. (Babiloni et al 2000 BIB007 , Cincotti et al 2001 , Dornhege et al 2004 , Fabiani et al 2004 , Gysels and Celka 2004 BIB004 BIB003     BIB008  BIB005 BIB009   BIB010 BIB002 BIB006 BIB001