A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Guidelines to Choose a <s> Publisher Summary This chapter discusses the role that the relationship between the number of measurements and the number of training patterns plays at various stages in the design of a pattern recognition system. The designer of a pattern recognition system should make every possible effort to obtain as many samples as possible. As the number of samples increases, not only does the designer have more confidence in the performance of the classifier, but also more measurements can be incorporated in the design of the classifier without the fear of peaking in its performance. However, there are many pattern classification problems where either the number of samples is limited or obtaining a large number of samples is extremely expensive. If the designer chooses to take the optimal Bayesian approach, the average performance of the classifier improves monotonically as the number of measurements is increased. Most practical pattern recognition systems employ a non-Bayesian decision rule because the use of optimal Bayesian approach requires knowledge of prior densities, and besides, their complexity precludes the development of real-time recognition systems. The peaking behavior of practical classifiers is caused principally by their nonoptimal use of measurements. <s> BIB001 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Guidelines to Choose a <s> The effects of sample size on feature selection and error estimation for several types of classifiers are discussed. The focus is on the two-class problem. Classifier design in the context of small design sample size is explored. The estimation of error rates under small test sample size is given. Sample size effects in feature selection are discussed. Recommendations for the choice of learning and test sample sizes are given. In addition to surveying prior work in this area, an emphasis is placed on giving practical advice to designers and users of statistical pattern recognition systems. > <s> BIB002 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Guidelines to Choose a <s> Electroencephalogram (EEG) pattern classification plays an important role in the domain of brain computer interface (BCI). Hidden Markov model (HMM) might be a useful tool in EEG pattern classification since EEG data is a multivariate time series data which contains noise and artifacts. In this paper we present methods for EEG pattern classification which jointly employ principal component analysis (PCA) and HMM. Along this line, two methods are introduced: (1) PCA+HMM; (2) PCA+HMM+SVM. Usefulness of principal component features and our hybrid method is confirmed through the classification of EEG that is recorded during the imagination of a left or right hand movement. <s> BIB003 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Guidelines to Choose a <s> In recent years, brain-computer interface (BCI) technology has emerged very rapidly. Brain-computer interfaces (BCIs) bring us a new communication interface technology which can translate brain activities into control signals of devices like computers, robots. The preprocessing of electroencephalographic (EEG) signal and translation algorithms play an important role in EEG-based BCIs. In this study, we employed an independent component analysis (ICA)-based preprocessing method and a committee machine-based translation algorithm for the offline analysis of a cursor control experiment. The results show that ICA is an efficient preprocessing method and the committee machine is a good choice for translation algorithm. <s> BIB004 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Guidelines to Choose a <s> In this paper we review classification algorithms used to design brain–computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI. <s> BIB005 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Guidelines to Choose a <s> In recent years, the detection of voluntary motor intentions from electroencephalogram (EEG) has been used for triggering external devices in closed-loop brain-computer interface (BCI) research. Movement-related cortical potentials (MRCP), a type of slow cortical potentials, have been recently used for detection. In order to enhance the efficacy of closed-loop BCI systems based on MRCPs, a manifold method called Locality Preserving Projection, followed by a linear discriminant analysis (LDA) classifier (LPP-LDA) is proposed in this paper to detect MRCPs from scalp EEG in real time. In an online experiment on nine healthy subjects, LPP-LDA statistically outperformed the classic matched filter approach with greater true positive rate (79 ± 11% versus 68 ± 10%; p = 0.007) and less false positives (1.4 ± 0.8/min versus 2.3 ± 1.1/min; p = 0.016). Moreover, the proposed system performed detections with significantly shorter latency (315 ± 165 ms versus 460 ± 123 ms; p = 0.013), which is a fundamental characteristics to induce neuroplastic changes in closed-loop BCIs, following the Hebbian principle. In conclusion, the proposed system works as a generic brain switch, with high accuracy, low latency, and easy online implementation. It can thus be used as a fundamental element of BCI systems for neuromodulation and motor function rehabilitation. <s> BIB006
Classifier. EEG signals are notably nonstationary so training sets acquired from different sessions are probable to be quite different. Consequently, a low variance (sensitivity to training set) can be a solution to tackle with the variability issue in some studies. Unstable classifiers tend to have a low bias (deviation between the estimated mapping and the superlative mapping) and a high variance, while stable classifiers have a high bias and a low variance BIB005 . This might be an explanation of why some simple classifiers like SVM, Matched Filter, and so forth sometimes surpass more than complex ones. Simple classifiers are generally slower than other classifiers but fast enough for real-time applications. Here the question is whether it is worthy to get higher performance at the expense of computational cost. In order to attain minimal classification error, both the variance and the bias must be small. Unfortunately, natural variancebias tradeoff is always present BIB005 . The classifier will probably give bad performance if the number of training data is lesser matched to the size of the feature vectors. Usage of at least five to ten times training samples per class as the dimensionality is recommended BIB002 BIB001 . Generally the training set is small and dimensionality is high so; unluckily this cannot be useful in all BCI systems. One of the reasons might be the long duration of the tasks, which on the other hand is hectic for subjects. Consequently this "curse" is a key concern in BCI design. Furthermore, combinations of classifiers also seem to be very efficient in some studies BIB006 BIB004 BIB003 . Normally experiments are performed in a controlled manner minimizing noise and other artifacts while presence of noise in real life scenario is quite obvious. One possible solution might be to increase the generalization abilities of the classifier.