TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> The synchronous BCI <s> Driven by the progress in the field of single-trial analysis of EEG, there is a growing interest in brain computer interfaces (BCIs), i.e., systems that enable human subjects to control a computer only by means of their brain signals. In a pseudo-online simulation our BCI detects upcoming finger movements in a natural keyboard typing condition and predicts their laterality. This can be done on average 100-230ms before the respective key is actually pressed, i.e., long before the onset of EMG. Our approach is appealing for its short response time and high classification accuracy (>96%) in a binary decision where no human training is involved. We compare discriminative classifiers like Support Vector Machines (SVMs) and different variants of Fisher Discriminant that possess favorable regularization properties for dealing with high noise cases (inter-trial variablity). <s> BIB001 </s> TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> The synchronous BCI <s> The reliable operation of brain-computer interfaces (BCIs) based on spontaneous electroencephalogram (EEG) signals requires accurate classification of multichannel EEG. The design of EEG representations and classifiers for BCI are open research questions whose difficulty stems from the need to extract complex spatial and temporal patterns from noisy multidimensional time series obtained from EEG measurements. The high-dimensional and noisy nature of EEG may limit the advantage of nonlinear classification methods over linear ones. This paper reports the results of a linear (linear discriminant analysis) and two nonlinear classifiers (neural networks and support vector machines) applied to the classification of spontaneous EEG during five mental tasks, showing that nonlinear classifiers produce only slightly better classification results. An approach to feature selection based on genetic algorithms is also presented with preliminary results of application to EEG during finger movement. <s> BIB002 </s> TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> The synchronous BCI <s> 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. <s> BIB003 </s> TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> The synchronous BCI <s> We report the implementation of a text input application (speller) based on the P300 event related potential. We obtain high accuracies by using an SVM classifier and a novel feature. These techniques enable us to maintain fast performance without sacrificing the accuracy, thus making the speller usable in an online mode. In order to further improve the usability, we perform various studies on the data with a view to minimizing the training time required. We present data collected from nine healthy subjects, along with the high accuracies (of the order of 95% or more) measured online. We show that the training time can be further reduced by a factor of two from its current value of about 20 min. High accuracy, fast learning, and online performance make this P300 speller a potential communication tool for severely disabled individuals, who have lost all other means of communication and are otherwise cut off from the world, provided their disability does not interfere with the performance of the speller. <s> BIB004 </s> TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> The synchronous BCI <s> Electroencephalogram(EEG) signal is an important information source of underlying brain processes.The communication based on EEG between human brain and computer is a new modality of human-computer interaction.In this paper,EEG signal of different mental tasks is preprocessed by Independent Component Analysis(ICA),AR model coefficient is extracted as feature vector,and classify the mental tasks based on BP neural network.According to the analysis and experiment results,the method can get high correct rate of classification <s> BIB005
The synchronous case is the most widely spread. Tables A1, A3, A4 and A5) . RFLDA shares several properties with SVM such as being a linear and regularized classifier. Its training algorithm is even very close to the SVM one. Consequently, it also reached very interesting results in some experiments BIB001 [39]. The first reason for this success may be regularization. Actually, BCI features are often noisy and likely to contain outliers  . Regularization may overcome this problem and increase the generalization capabilities of the classifier. As a consequence, regularized classifiers, and more particularly linear SVM, have outperformed unregularized ones of the same kind, i.e., LDA, during several BCI studies  [38] BIB003 . Similarly a nonlinear SVM has outperformed an unregularized nonlinear classifier, namely, an MLP, in another BCI study BIB002 . The second reason may be the simplicity of SVM. Indeed, the decision rule of SVM is a simple linear function in the kernel space which makes SVM stable and therefore, have a low Variance. Since BCI features are very unstable over time, having a low Variance may also be a key for low classification error in BCI. The last reason probably is the robustness of SVM with respect to the curse-ofdimensionality (see Section 2.1.1). This has enabled SVM to obtain very good results even with very high dimensional feature vectors and a small training set BIB003 [19]. However, SVM are not drawback free for BCI as they generally are slower than other classifiers. Luckily, they are fast enough for real-time BCI, e.g., BIB004 . Table A1 ). BIB005 is an exception, but the authors admitted that the chosen HMM architecture may not have been suitable. Dynamic classifiers probably are successfull in BCI because they can catch the relevant temporal variations present in the extracted features. Futhermore, classifying a sequence of low dimensional feature vectors, instead of a very high dimensional one, in a way, solves the curse-of-dimensionality. Finally, using dynamic classifiers in synchronous BCI also solves the problem of finding the optimal instant for classification as the whole time sequence is classified and not just a particular time window  .