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:  Post-processing 
1. Abstract of Cited Paper (BIB001):  We describe a brain-computer interface (BCI) system, which uses a set of adaptive linear preprocessing and classification algorithms for single-trial detection of error related negativity (ERN). We use the detected ERN as an estimate of a subject's perceived error during an alternative forced choice visual discrimination task. The detected ERN is used to correct subject errors. Our initial results show average improvement in subject performance of 21% when errors are automatically corrected via the BCI. We are currently investigating the generalization of the overall approach to other tasks and stimulus paradigms. 
2. Abstract of Cited Paper (BIB002):  Real time spike detection is an important requirement for developing brain machine interfaces (BMIs). We examined three classes of spike-detection algorithms to determine which is best suited for a wireless BMI with a limited transmission bandwidth and computational capabilities. The algorithms were analyzed by tabulating true and false detections when applied to a set of realistic artificial neural signals with known spike times and varying signal to noise ratios. A design-specific cost function was developed to score the relative merits of each detector; correct detections increased the score, while false detections and computational burden reduced it. Test signals both with and without overlapping action potentials were considered. We also investigated the utility of rejecting spikes that violate a minimum refractory period by occurring within a fixed time window after the preceding threshold crossing. Our results indicate that the cost-function scores for the absolute value operator were comparable to those for more elaborate nonlinear energy operator based detectors. The absolute value operator scores were enhanced when the refractory period check was used. Matched-filter-based detectors scored poorly due to their relatively large computational requirements that would be difficult to implement in a real-time system. 
3. Abstract of Cited Paper (BIB003):  Nearly all electroencephalogram (EEG)-based brain-computer interface (BCI) systems operate in a cue-paced or synchronous mode. This means that the onset of mental activity (thought) is externally-paced and the EEG has to be analyzed in predefined time windows. In the near future, BCI systems that allow the user to intend a specific mental pattern whenever she/he wishes to produce such patterns will also become important. An asynchronous BCI is characterized by continuous analyzing and classification of EEG data. Therefore, it is important to maximize the hits (true positive rate) during an intended mental task and to minimize the false positive detections in the resting or idling state. EEG data recorded during right/left motor imagery is used to simulate an asynchronous BCI. To optimize the classification results, a refractory period and a dwell time are introduced. 
4. Abstract of Cited Paper (BIB004):  Abstract This case study demonstrates the coupling of an electroencephalogram (EEG)-based Brain–Computer Interface (BCI) with an implanted neuroprosthesis (Freehand ® system). Because the patient was available for only 3 days, the goal was to demonstrate the possibility of a patient gaining control over the motor imagery-based Graz BCI system within a very short training period. By applying himself to an organized and coordinated training procedure, the patient was able to generate distinctive EEG-patterns by the imagination of movements of his paralyzed left hand. These patterns consisted of power decreases in specific frequency bands that could be classified by the BCI. The output signal of the BCI emulated the shoulder joystick usually used, and by consecutive imaginations the patient was able to switch between different grasp phases of the lateral grasp that the Freehand ® system provided. By performing a part of the grasp-release test, the patient was able to move a simple object from one place to another. The results presented in this work give evidence that Brain–Computer Interfaces are an option for the control of neuroprostheses in patients with high spinal cord lesions. The fact that the user learned to control the BCI in a comparatively short time indicates that this method may also be an alternative approach for clinical purposes.

Literature Review:
Post-processing techniques can be utilized in most of the BCI designs to decrease the error rates. Some post-processing techniques can be designed specifically for a target application. For example, when a BCI system is used to activate a spelling device, some letters can be omitted without losing information. The system can also take into consideration the conditional probabilities of letters provided by one or two preceding letters and make corresponding suggestions to the patient (Kubler et al 1999) . Such techniques may also be feasible for other applications and consequently increase the performance of the BCI systems. There is a possibility that just after the end of a trial, some features of the brain signal reveal whether or not the trial was successful (that is, whether the outcome was or was not what the subject desired). These features are referred to as error potentials and can be used to detect errors in a BCI system and void the outcome. This error detection approach was encouraged by evidence that errors in conventional motor performances have detectable effects on the EEG recorded just after the error occurs (Falkenstein et al 1995 , Falkenstein et al 2001 , Gehring et al 1995 . Whatever the nature of the error potential, the central decision for a BCI is how useful the error potential can be in detecting errors in single trials, and thereby improving accuracy. While its signal-to-noise ratio (SNR) is low, the error potential can improve the performance of a BCI system. In the meantime, better methods for recognizing and measuring the error potential could substantially improve its SNR, and thereby increase its impact on accuracy of a BCI system. Such error potentials have been used in a few BCI systems to increase the performance (Bayliss et al 2004 , Blankertz et al 2002b  BIB002 BIB001 . Another useful technique in decreasing false activations of BCI systems is to consider a measure of confidence in classification. In such a case, the output of the system can only be activated when the probability of the output being in an active state is greater than a given probability threshold or some criterion. Otherwise, the response of the BCI is considered 'unknown' and rejected to avoid making risky decisions. This is a useful way of reducing false decisions of the system (e.g., Cincotti et al (2003b) ,  and Penny et al (2000) ) and might be used in any BCI design. Considering mechanisms like debouncing the output of BCI designs also can reduce the number of false activations (Bashashati et al 2005 , Borisoff et al 2004 , Fatourechi et al 2004  BIB005 BIB003 BIB004 . These methods are specifically useful for so called asynchronous (self-paced) BCIs. Since false positives could happen in periods longer than just a few samples, using the debouncing technique in a manner similar to the debouncing of physical switches is expected to improve false activation rates (but with a cost in decreased re-activation time). The debounce component continuously monitors the output of the classifier. After an activation is detected (e.g. a change in logical state from '0' to '1' in a binary classifier), the output is activated for one time sample, then the output is forced to an inactive state for Td − 1 time samples, where Td is the debounce time period in samples. In some studies this time period is referred to as refractory period. As the debounce period is increased, the false activation rate is decreased for a given true positive rate. However, with increasing this time period, the re-activation time of the BCI system is impacted. The trade-off is clear and one needs to consider this for a given application.