A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> The feasibility of establishing an alternative mode of communication between man and his surroundings was studied. The form of communication proposed uses only the subject's brain waves, with no overt physical action required. The subject's electroencephalograms (EEG) were recorded while various mental tasks designed to elicit hemispheric responses were performed. Features formed from the EEG recording were then used as inputs into a Bayes quadratic classifier to test classification accuracy between the various tasks. The results obtained indicate that it is possible to accurately distinguish between any two of the five tasks investigated. A comparison between three different methods for creating the feature sets is also presented. > <s> BIB001 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> Planning and preparing voluntary finger movements result in a specific topographical pattern of event-related desynchronization (ERD) in the alpha band. These patterns generated for planning left and right hand movements show large enough differences to be seperable by a Learning Vector Quantizer (LVQ). This paper describes one-dimensional control of a cursor on a computer monitor based on the differentiation of two cortical activation patterns measured by two EEG channels. <s> BIB002 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> This paper studies the potential of six artificial intelligence (AI)-based approaches for classification of non-averaged EEG data that is on important issue for the building of a brain-computer interface (BCI). The effectiveness of the methods in terms of classification accuracy, stability and speed is reported. <s> BIB003 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> This paper describes a simple but very powerful method for feature selection. The Distinction Sensitive Learning Vector Quantizer (DSLVQ) is a learning classifier which focuses on relevant features according to its own instance based classifications. Two different experiments describe the application of DSLVQ as a feature selector for an EEG-based Brain Computer Interface (BCI) system. It is shown that optimal electrode positions as well as frequency bands are strongly dependent on each subject and that a subject specific feature selection is when important for BCI systems. <s> BIB004 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> 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. <s> BIB005 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> In an online EEG discrimination task, continuous feedback was presented. The EEG was recorded during imagination of left and right hand movement and analyzed with adaptive autoregressive parameters. The parameter discrimination was fed back in form of a rectangular bar on a computer screen over a period of four seconds. An online classification result of more than 90% was obtained after a few sessions. <s> BIB006 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> 2 ABSTRACT: Left and right hand movement imagery is accompanied by an EEG event-related desynchronization (ERD) over the contralateral hand area. The time courses of the ERD from two brain-computer interface experiments were investigated by the calculation of instantaneous band power changes and by adaptive autoregressive model parameters combined with linear discriminant analysis. Subject specific differences of the EEG reactivity patterns were observed. <s> BIB007 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> 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. <s> BIB008 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> Electroencephalogram (EEG) recordings during right and left motor imagery can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communication channel to replace an impaired motor function. It can be used by, e.g., patients with amyotrophic lateral sclerosis (ALS) to develop a simple binary response in order to reply to specific questions. Four subjects participated in a series of on-line sessions with an EEG-based cursor control. The EEG was recorded from electrodes overlying sensory-motor areas during left and right motor imagery. The EEG signals were analyzed in subject-specific frequency bands and classified on-line by a neural network. The network output was used as a feedback signal. The on-line error (100%-perfect classification) was between 10.0 and 38.1%. In addition, the single-trial data were also analyzed off-line by using an adaptive autoregressive (AAR) model of order 6. With a linear discriminant analysis the estimated parameters for left and right motor imagery were separated. The error rate, obtained varied between 5.8 and 32.8% and was, on average, better than the on-line results. By using the AAR-model for on-line classification an improvement in the error rate can be expected, however, with a classification delay around 1 s. <s> BIB009 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> In this study we compared two spectral estimation methods for feature extraction in development of computer access for communication and control based on electroencephalographic (EEG) signals. We compared power spectrum calculated by fast Fourier transform (FFT) and auto regressive (AR) coefficients calculated by Burg's algorithm. We analyzed four surface EEG signals recorded above sensory- motor areas, while the subject was attempting to use only mental activities to modulate his EEG signals resulting in desired simple movements of animated object on the feedback computer screen. To establish communication channel based on EEG signals, we asked our subject to determine which mental activity produced reproducible control actions. During the on- line training we used AR coefficients of recorded EEG signals for the feedback generating variables, i.e. the object's movement direction and speed were dependent on the EEG pattern. Our subject was able to determine which mental activity resulted in desired movements and to learn to control object movements with more than 90% accuracy. We performed off-line analysis to determine if FFT or AR can be more successful in extracting relevant information from the EEG signals before performing pattern recognition by a machine learning classifier. Probability of misclassification was used as the outcome measure. Off-line analysis showed that the overall best classification accuracy achieved with AR exceeds that achieved with FFT. <s> BIB010 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> EEG feedback studies demonstrate that human subjects can learn to regulate electrocortical activity over the sensorimotor cortex. Such self-induced EEG changes could serve as control signals for a Brain Computer Interface. The experimental task of the current study was to imagine either right-hand or left-hand movement depending on a visual cue stimulus on a computer monitor. The performance of this imagination task was controlled on-line by means of a feedback bar that represented the current EEG pattern. EEG signals recorded from left and right central recording sites were used for on-line classification. For the estimation of EEG parameters, an adaptive autoregressive model was applied, and a linear discriminant classifier was used to discriminate between EEG patterns associated with left and right motor imagery. Four trained subjects reached 85% to 95% classification accuracy in the course of the experimental sessions. To investigate the impact of continuous feedback presentation, time courses of band power changes were computed for subject-specific frequency bands. The EEG data revealed a significant event-related desynchronization over the contralateral central area in all subjects. Two subjects simultaneously displayed synchronization of EEG activity (event-related synchronization) over the ipsilateral side. During feedback presentation the event-related desynchronization/event-related synchronization patterns showed increased hemispheric asymmetry compared to initial control sessions without feedback. <s> BIB011 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> A new communication channel for severely handicapped people could be opened with a direct brain to computer interface (BCI). Such a system classifies electrical brain signals online. In a series of training sessions, where electroencephalograph (EEG) signals are recorded on the intact scalp, a classifier is trained to discriminate a limited number of different brain states. In a subsequent series of feedback sessions, where the subject is confronted with the classification results, the subject tries to reduce the number of misclassifications. In this study the relevance of different spectral components is analyzed: (1) on the training sessions to select optimal frequency bands for the feedback sessions and (2) on the feedback sessions to monitor changes. <s> BIB012 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> Abstract Background : EEG-based communication could be a valuable new augmentative communication technology for those with severe motor disabilities. Like all communication methods, it faces the problem of errors in transmission. In the Wadsworth EEG-based brain-computer interface (BCI) system, subjects learn to use mu or beta rhythm amplitude to move a cursor to targets on a computer screen. While cursor movement is highly accurate in trained subjects, it is not perfect. Methods : In an effort to develop a method for detecting errors, this study compared the EEG immediately after correct target selection to that after incorrect selection. Results : The data showed that a mistake is followed by a positive potential centered at the vertex that peaks about 180 ms after the incorrect selection. Conclusion : The results suggest that this error potential might provide a method for detecting and voiding errors that requires no additional time and could thereby improve the speed and accuracy of EEG-based communication. <s> BIB013 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> 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. <s> BIB014 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> 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. <s> BIB015 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> Abstract Objectives : Severely paralyzed patients could learn to voluntarily generate slow cortical potential (SCP) shifts in their electroencephalogram and to use these signals to operate a communication device. To enhance the patients' autonomy, the present study describes the development of a permanently available communication system that can be turned on and off by locked-in patients without external assistance. A skill necessary for turning the system on is the ability to regulate one's slow potentials in the absence of continuous feedback. Methods : A stepwise learning approach was employed to train two paralyzed patients to regulate their SCPs without continuous feedback. Elements of the original communication system were gradually removed and elements of the new stand-by mode were introduced. Results : At the end of the learning procedure, both patients achieved correct response rates of above 84% in training sessions without continuous feedback. This skill enabled them to turn the communication device on and off without assistance from others. Conclusions : These findings suggest that severely paralyzed individuals can learn to operate an EEG-based communication device autonomously. <s> BIB016 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> Parametric modelling strategies and spectral analysis are explored in conjunction with linear discriminant analysis to facilitate an EEG based direct-brain interface for use by disabled people. A self-paced typing exercise is analysed by employing for feature extraction, respectively, an autoregressive model, an autoregressive with exogenous input model, and a time-frequency decomposition of the data. Modelling both the signal and noise is found to be more, effective than modelling the noise alone with the former yielding an accuracy of 70.7% and the latter an accuracy of 57.4%. Experiments, using the raw samples of a short-time power spectral density estimate of each trial as features, yielded an accuracy of 62.5%. <s> BIB017 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> There is a growing interest in the use of physiological signals for communication and operation of devices for the severely motor disabled as well as for healthy people. A few groups around the world have developed brain-computer interfaces (BCIs) that rely upon the recognition of motor-related tasks (i.e., imagination of movements) from on-line EEG signals. In this paper we seek to find and analyze the set of relevant EEG features that best differentiate spontaneous motor-related mental tasks from each other. This study empirically demonstrates the benefits of heuristic feature selection methods for EEG-based classification of mental tasks. In particular, it is shown that the classifier performance improves for all the considered subjects with only a small proportion of features. Thus, the use of just those relevant features increases the efficiency of the brain interfaces and, most importantly, enables a greater level of adaptation of the personal BCI to the individual user. <s> BIB018 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> This paper proposes a novel and simple local neural classifier for the recognition of mental tasks from on-line spontaneous EEG signals. The proposed neural classifier recognizes three mental tasks from on-line spontaneous EEG signals. Correct recognition is around 70%. This modest rate is largely compensated by two properties, namely low percentage of wrong decisions (below 5%) and rapid responses (every 1/2 s). Interestingly, the neural classifier achieves this performance with a few units, normally just one per mental task. Also, since the subject and his/her personal interface learn simultaneously from each other, subjects master it rapidly (in a few days of moderate training). Finally, analysis of learned EEG patterns confirms that for a subject to operate satisfactorily a brain interface, the latter must fit the individual features of the former. <s> BIB019 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> 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. <s> BIB020 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> The Graz-brain-computer interface (BCI) is a cue-based system using the imagery of motor action as the appropriate mental task. Relevant clinical applications of BCI-based systems for control of a virtual keyboard device and operations of a hand orthosis are reported. Additionally, it is demonstrated how information transfer rates of 17 b/min can be acquired by real time classification of oscillatory activity. <s> BIB021 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> By the use of a brain-computer interface (BCI), it is possible for completely paralyzed patients, who have lost their ability to speak, to have a new possibility to communicate with their environment. The training with such a BCI system can be performed at the patient's home, if there is a responsible person present who is familiar with the system. This person has to adjust different parameters and to adapt the training individually to each patient. Since this function is usually taken over by the developers of the system, the number of patients who can be included in regular BCI training is restricted due to geographical distances. This paper describes the implementation of a telemonitoring system, which makes it possible for the developer to control and supervise the BCI training from his or her own place of work. First experiences with a patient living far away from the developer's lab are reported. <s> BIB022 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> Abstract Objective : This case study describes how a completely paralyzed patient, diagnosed with severe cerebral palsy, was trained over a period of several months to use an electroencephalography (EEG)-based brain–computer interface (BCI) for verbal communication. Methods : EEG feedback training was performed in the patient's home (clinic), supervised from a distant laboratory with the help of a ‘telemonitoring system'. Online feedback computation was based on single-trial analysis and classification of specific band power features of the spontaneous EEG. Task-related changes in brain oscillations over the course of training steps was investigated by quantifying time–frequency maps of event-related (de-)synchronization (ERD/ERS). Results : The patient learned to ‘produce' two distinct EEG patterns, beta band ERD during movement imagery vs. no ERD during relaxing, and to use this for BCI-controlled spelling. Significant learning progress was found as a function of training session, resulting in an average accuracy level of 70% (correct responses) for letter selection. ‘Copy spelling' was performed with a rate of approximately one letter per min. Conclusions : The proposed BCI training procedure, based on electroencephalogram (EEG) biofeedback and concomitant adaptation of feature extraction and classification, may improve actual levels of communication ability in locked-in patients. ‘Telemonitoring-assisted' BCI training facilitates clinical application in a larger number of patients. <s> BIB023 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> The aim of the present study was to demonstrate the first time the non-invasive restoration of hand grasp function in a tetraplegic patient by electroencephalogram (EEG)-recording and functional electrical stimulation (FES) using surface electrodes. The patient was able to generate bursts of beta oscillations in the EEG by imagination of foot movement. These beta bursts were analyzed and classified by a brain-computer interface (BCI) and the output signal used to control a FES device. The patient was able to grasp a cylinder with the paralyzed hand. <s> BIB024 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> The ability to control electroencephalographic rhythms and to map those changes to the actuation of mechanical devices provides the basis for an assistive brain-computer interface (BCI). In this study, we investigate the ability of subjects to manipulate the sensorimotor mu rhythm (8-12-Hz oscillations recorded over the motor cortex) in the context of a rich visual representation of the feedback signal. Four subjects were trained for approximately 10 h over the course of five weeks to produce similar or differential mu activity over the two hemispheres in order to control left or right movement in a three-dimensional video game. Analysis of the data showed a steep learning curve for producing differential mu activity during the first six training sessions and leveling off during the final four sessions. In contrast, similar mu activity was easily obtained and maintained throughout all the training sessions. The results suggest that an intentional BCI based on a binary signal is possible. During a realistic, interactive, and motivationally engaging task, subjects learned to control levels of mu activity faster when it involves similar activity in both hemispheres. This suggests that while individual control of each hemisphere is possible, it requires more learning time. <s> BIB025 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> An improvement of the information transfer rate of brain-computer communication is necessary for the creation of more powerful and convenient applications. This paper presents an asynchronously controlled three-class brain-computer interface-based spelling device [virtual keyboard (VK)], operated by spontaneous electroencephalogram and modulated by motor imagery. Of the first results of three able-bodied subjects operating the VK, two were successful, showing an improvement of the spelling rate /spl sigma/, the number of correctly spelled letters/min, up to /spl sigma/=3.38 (average /spl sigma/=1.99). <s> BIB026 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> 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. <s> BIB027 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> EEG-based brain computer interface (BCI) provides a completely new communication channel between human brain and computer. Classification of EEG signals is a difficult task, especially when the classification has to be preformed on a single-trial EEG to continuously control a device. Event related desynchronization (ERD) has proven to be induced on the contralateral sensorimotor area during imagination of a left or right hand movement. In this paper, we introduced a quantification of ERD, with which a lower classification error rate and a higher information transfer rate can be obtained. The performance was tested by the Graz dataset for BCI competition 2003. <s> BIB028 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> In this paper, we have designed a two-state brain–computer interface (BCI) using neural network (NN) classification of autoregressive (AR) features from electroencephalogram (EEG) signals extracted during mental tasks. The main purpose of the study is to use Keirn and Aunon's data to investigate the performance of different mental task combinations and different AR features for BCI design for individual subjects. In the experimental study, EEG signals from five mental tasks were recorded from four subjects. Different combinations of two mental tasks were studied for each subject. Six different feature extraction methods were used to extract the features from the EEG signals: AR coefficients computed with Burg's algorithm, AR coefficients computed with a least-squares (LS) algorithm and adaptive autoregressive (AAR) coefficients computed with a least-mean-square (LMS) algorithm. All the methods used order six applied to 125 data points and these three methods were repeated with the same data but with segmentation into five segments in increments of 25 data points. The multilayer perceptron NN trained by the back-propagation algorithm (MLP-BP) and linear discriminant analysis (LDA) were used to classify the computed features into different categories that represent the mental tasks. We compared the classification performances among the six different feature extraction methods. The results showed that sixth-order AR coefficients with the LS algorithm without segmentation gave the best performance (93.10%) using MLP-BP and (97.00%) using LDA. The results also showed that the segmentation and AAR methods are not suitable for this set of EEG signals. We conclude that, for different subjects, the best mental task combinations are different and proper selection of mental tasks and feature extraction methods are essential for the BCI design. <s> BIB029 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> Brain activity recorded noninvasively is sufficient to control a mobile robot if advanced robotics is used in combination with asynchronous electroencephalogram (EEG) analysis and machine learning techniques. Until now brain-actuated control has mainly relied on implanted electrodes, since EEG-based systems have been considered too slow for controlling rapid and complex sequences of movements. We show that two human subjects successfully moved a robot between several rooms by mental control only, using an EEG-based brain-machine interface that recognized three mental states. Mental control was comparable to manual control on the same task with a performance ratio of 0.74. <s> BIB030 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> 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. <s> BIB031 </s> A survey of signal processing algorithms in brain?computer interfaces based on electrical brain signals <s> Discussion <s> Brain–computer interfaces (BCIs) can be realized on the basis of steady-state evoked potentials (SSEPs). These types of brain signals resulting from repetitive stimulation have the same fundamental frequency as the stimulation but also include higher harmonics. This study investigated how the classification accuracy of a 4-class BCI system can be improved by incorporating visually evoked harmonic oscillations. The current study revealed that the use of three SSVEP harmonics yielded a significantly higher classification accuracy than was the case for one or two harmonics. During feedback experiments, the five subjects investigated reached a classification accuracy between 42.5% and 94.4%. <s> BIB032
Several points raised in the previous section deserve further comment. Figure 2 summarizes the information in tables 3, 4 and 8, which respectively address signal enhancement, feature selection/dimensionality reduction and post-processing algorithms in BCI designs. Specifically, this figure shows the number of BCI designs that use specific signal enhancement, feature selection/dimensionality reduction and post-processing techniques. (Babiloni et al 2000  BIB020 , Blanchard and Blankertz 2004 , Boostani and Moradi 2004 , Cho et al 2004 , Cincotti et al 2001 BIB021 , Coyle et al 2005 , Fabiani et al 2004  , Garcia et al 2003b , Garrett et al 2003 , Guger et al 2000b BIB003 BIB002 BIB017 , Krauledat et al 2004 , Krausz et al 2003 , Kubler et al 2005 , Lal et al 2004 , Leeb and Pfurtscheller 2004 , Lemm et al 2005 , Mahmoudi and Erfanian 2002 , Mason and Birch 2000 BIB008 BIB005   BIB018 BIB022 BIB031 BIB023   BIB014 BIB024 BIB021 BIB025 BIB004 BIB013 BIB026 , Sheikh et al 2003 BIB027 , Trejo et al 2003 BIB028 , Wolpaw et al 1991  , Wolpaw and McFarland 1994  (Burke et al 2002  , Graimann et al 2003b  BIB021 , Haselsteiner and Pfurtscheller 2000   , Lal et al 2004 BIB011  BIB020 BIB015 BIB009  BIB014 BIB006 BIB007 (Birbaumer et al 1999 BIB016 , Kubler et al 1998 BIB020   (Anderson et al 1995b , Garrett et al 2003 BIB029 BIB001 , Kostov and Polak 2000  BIB010 Baier 2005, Mensh et al 2004) a Designs that differentiate between relaxed state and movement tasks are considered in 'sensorimotor activity + response to mental tasks' category. In the remainder of this section we highlight the top three or four methods that have been used in the signal processing blocks of BCI systems (as introduced in section 2). Of the 96 BCI designs that employ signal enhancement techniques before extracting the features from the signal, 32% use surface Laplacian (SL), 22% use either principal component analysis (PCA) or independent component analysis (ICA), 14% use common spatial patterns (CSP) and 11% use common average referencing (CAR) techniques. Thirty-eight of the reported BCI designs employ feature selection/dimensionality reduction algorithms; 26% of these 38 designs use genetic algorithms (GA), 24% use distinctive sensitive learning vector quantization (DSLVQ), and 13% use PCA. Of the 30 BCI designs that use post-processing algorithms to reduce the amount of error in the output of the BCI system, 57% use averaging techniques and consider rejecting activations that have low certainty, 27% consider using the debounce block (or refractory period) to deactivate the output for a short period of time when a false activation is detected, and 16% use event-related negativity (ERN) signals to detect error activations. Figure 3 summarizes the results presented in table 5, and shows the number of BCI designs that are based on  BIB003 BIB002 BIB014 BIB012  BIB022 BIB032 BIB011  BIB020 BIB009 BIB021  BIB014 BIB006 BIB007  BIB030 ) SSP (Babiloni et al 2000  BIB020 , Cincotti et al 2001  ) SOM-based SSP  BIB019 (Birbaumer et al 1999 BIB016 , Kubler et al 1998 BIB020   ) Linear combination − continuous feedback (Fabiani et al 2004 , Krausz et al 2003 , Kubler et al 2005 BIB008 BIB005   BIB013 , Sheikh et al 2003 , Wolpaw and McFarland 1994  BIB005  BIB011 a Regularization may be applied before LDA classification scheme. sensorimotor activity, SCP, VEP, P300, activity of neural cells, 'response to mental tasks' and multiple neuromechanisms and use different feature extraction techniques. Based on the results of figure 3, 41% of the BCIs that are based on the sensorimotor activity use power-spectral-density features, 16% rely on parametric modeling of the data, 13% use time-frequency representation (TFR) methods and 6% do not employ any feature extraction methods. 74% of the SCP-based BCI designs calculate SCP signals using low-pass filtering methods, and 64% of the VEP-based BCIs use power-spectral features at specific frequencies. 26% of the BCIs based on P300 calculate the peaks of the signal in a specific time window to detect the P300 component of the EEG; 22% use TFR-based methods, 22% use no feature extraction method, and 15% use cross-correlation with a specific template. 41% of the BCI designs that use mental tasks to control a BCI use powerspectral features and 37% use parametric modeling of the input signal. As most of the BCI designs that are based on neural cortical recordings mainly try to model the direct relationship between the neural cortical recordings and movements, they do not use a feature-extraction algorithm. 45% of the BCI designs that are based on multiple neuromechanisms rely on power-spectral features, 17% use parametric modeling, and 17% use time-frequency representation (TFR) methods. Summarizing tables 6 and 7, the number of BCI designs that use different feature classification algorithms are shown in figure 4. About 75% of the BCI designs use classification schemes that are not based on neural networks (NN). These are composed of those methods that 