TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> Other Neural Network architectures <s> The self-organized map, an architecture suggested for artificial neural networks, is explained by presenting simulation experiments and practical applications. The self-organizing map has the property of effectively creating spatially organized internal representations of various features of input signals and their abstractions. One result of this is that the self-organization process can discover semantic relationships in sentences. Brain maps, semantic maps, and early work on competitive learning are reviewed. The self-organizing map algorithm (an algorithm which order responses spatially) is reviewed, focusing on best matching cell selection and adaptation of the weight vectors. Suggestions for applying the self-organizing map algorithm, demonstrations of the ordering process, and an example of hierarchical clustering of data are presented. Fine tuning the map by learning vector quantization is addressed. The use of self-organized maps in practical speech recognition and a simulation experiment on semantic mapping are discussed. > <s> BIB001 </s> TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> Other Neural Network architectures <s> A neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors, which may represent fuzzy or crisp sets of features. The architecture, called fuzzy ARTMAP, achieves a synthesis of fuzzy logic and adaptive resonance theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Four classes of simulation illustrated fuzzy ARTMAP performance in relation to benchmark backpropagation and generic algorithm systems. These simulations include finding points inside versus outside a circle, learning to tell two spirals apart, incremental approximation of a piecewise-continuous function, and a letter recognition database. The fuzzy ARTMAP system is also compared with Salzberg's NGE systems and with Simpson's FMMC system. > <s> BIB002 </s> TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> Other Neural Network architectures <s> The existence of characteristic changes in the EEG of a subject preparing for the execution of a voluntary movement is reviewed. Some of those changes, i.e., the "readiness potentials" (RPs), are suggested as a possible link between the internal processes associated with different forms of motion preparation and a digital signal processing system designed to classify the different intended movements. Such a system can potentially be used to provide input to a specialized graphic user interface, constituting a brain-computer interface (BCI). Our current work on neural-network classification of RPs is summarized and the results are presented. <s> BIB003 </s> TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> Other Neural Network architectures <s> 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. <s> BIB004 </s> TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> Other Neural Network architectures <s> A new parallel man-machine training approach to brain-computer interface (BCI) succeeded through a unique application of machine learning methods. The BCI system could train users to control an animated cursor on the computer screen by voluntary electroencephalogram (EEG) modulation. The authors' BCI system requires only two to four electrodes, and has a relatively short training time for both the user and the machine. Moving the cursor in one dimension, the authors' subjects were able to hit 100% of randomly selected targets, while in two dimensions, accuracies of approximately 63% and 76% was achieved with the authors' two subjects. <s> BIB005 </s> TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> Other Neural Network architectures <s> This paper introduces a neural network architecture for classifying feature vectors symbolizing portions (or segments) of an electroencephalogram (EEG) trace of a human subject. This classification task is the one that is typically required when developing a so-called brain-computer interface (BCI), which analyzes the EEG signals of a subject in order to "understand" the subject's thoughts. However, instead of merely saying which "category of thoughts" (i.e., which class) the respective input feature vector belongs to, the network described here estimates the probabilities of an EEG segment being associated with each individual class. The network, which is called PeGNC (for probability estimating guarded neural classifier), is tested with two kinds of experiments. In the first experiment, the /spl alpha/-rhythm associated with a human subject closing the eyes is detected online with the help of a frequency-based representation. Since the EEG signal is, in general, always a mixture of numerous action potentials generated simultaneously and it is, thus, very likely that mental activities result in overlapping classes, it is reasonable to believe that the PeGNC network - which does not select any one single class, but determines probability values for each mental category - is particularly suitable for this kind of EEG analysis. The second experiment deals with this issue on the basis of an offline analysis of simulated data. <s> BIB006 </s> TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces <s> Other Neural Network architectures <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> BIB007
Other types of NN architecture are used in the field of BCI. Among them, one deserves a specific attention as it has been specifically created for BCI: the Gaussian classifier  BIB007 . Each unit of this NN is a Gaussian discriminant function representing a class prototype. According to its authors, this NN outperforms MLP on BCI data and can perform efficient rejection of uncertain samples  . As a consequence, this classifier has been applied with success to motor imagery  and mental task classification  , particularly during asynchronous experiments   . Besides the Gaussian classifier, several other NN have been applied to BCI purposes, in a more marginal way. They are not described here, due to space limitations: • Learning Vector Quantization (LVQ) Neural Network BIB001 [54] • Fuzzy ARTMAP Neural Network BIB002 [56]; • Dynamic Neural Networks such as the Finite Impulse Response Neural Network (FIRNN)  , Time-Delay Neural Network (TDNN) or Gamma dynamic Neural Network (GDNN) BIB003 ; • RBF Neural Network  [58]; • Bayesian Logistic Regression Neural Network (BLRNN) BIB004 ; inria-00134950, version 1 -6 Mar 2007 • Adaptive Logic Network (ALN) BIB005 ; • Probability estimating Guarded Neural Classifier (PeGNC) BIB006 .