Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Convergence of Probability Measures

Changepoints are abrupt variations in the generative parameters of a data sequence. Online detection of changepoints is useful in modelling and prediction of time series in application areas such as finance, biometrics, and robotics. While frequentist methods have yielded online filtering and prediction techniques, most Bayesian papers have focused on the retrospective segmentation problem. Here we examine the case where the model parameters before and after the changepoint are independent and we derive an online algorithm for exact inference of the most recent changepoint. We compute the probability distribution of the length of the current ``run,'' or time since the last changepoint, using a simple message-passing algorithm. Our implementation is highly modular so that the algorithm may be applied to a variety of types of data. We illustrate this modularity by demonstrating the algorithm on three different real-world data sets.

Bayesian Online Changepoint Detection

Selective review of offline change point detection methods

The ERP PCA Toolkit: an open source program for advanced statistical analysis of event-related potential data.

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG.

http://brain.bio.msu.ru/papers/Cichocki_Shishkin_Musha_Leonowicz_Asada_Kurachi_2005_ClinNeurophys_BSS_Filt_AD_correctedProof.pdf

EEG filtering based on blind source separation (BSS) for early detection of Alzheimer's disease

The P300-based brain-computer interface (P300 BCI) is currently a very popular topic in assistive technology development. However, only a few simple P300 BCI-based games have been designed so far. Here, we analyze the shortcomings of this BCI in gaming applications and show that solutions for overcoming them already exist, although these techniques are dispersed over several different games. Additionally, new approaches to improve the P300 BCI accuracy and flexibility are currently being proposed in the more general P300 BCI research. The P300 BCI, even in its current form, not only exhibits relatively high speed and accuracy, but also can be used without user training, after a short calibration. Taking these facts together, the broader use of the P300 BCI in BCI-controlled video games is recommended.

Adapting the P300-Based Brain–Computer Interface for Gaming: A Review

This chapter is devoted to one of the most interesting applications of non-parametric statistical diagnosis, namely, to the analysis of the human brain’s electrical activity (the electroencephalogram, or EEG). The meaning and the features of the EEG, as well as the problems arising from the high non-stationarity of the EEG signal, are reviewed. We present experimental results demonstrating the application of the statistical diagnosis methods described in this book to the EEG, and discuss the prospects for further development of the change-point detection methodology with the emphasis on the estimation of coupling between different signal channels.

Application of the change-point analysis to the investigation of the brain’s electrical activity

http://brain.bio.msu.ru/papers/leonowicz_karvanen_shishkin_2005_jneuroscimeth_trimmedaveraging_draft.pdf

Trimmed estimators for robust averaging of event-related potentials.

The early detection Alzheimer’s disease (AD) is an important challenge. In this paper, we propose a novel method for early detection of AD using electroencephalographic (EEG) recordings: first a blind source separation algorithm is applied to extract the most significant spatio-temporal components; these components are subsequently wavelet transformed; the resulting time-frequency representation is approximated by sparse “bump modeling”; finally, reliable and discriminant features are selected by orthogonal forward regression and the random probe method. These features are fed to a simple neural network classifier. The method was applied to EEG recorded in patients with Mild Cognitive Impairment (MCI) who later developed AD, and in age-matched controls. This method leads to a substantially improved performance (93% correctly classified, with improved sensitivity and specificity) over classification results previously published on the same set of data. The method is expected to be applicable to a wide variety of EEG classification problems.

Sergei L. Shishkin

Papers

EEG filtering based on blind source separation (BSS) for early detection of Alzheimer's disease

Adapting the P300-Based Brain–Computer Interface for Gaming: A Review

Application of the change-point analysis to the investigation of the brain’s electrical activity

Trimmed estimators for robust averaging of event-related potentials.

Early detection of alzheimer’s disease by blind source separation, time frequency representation, and bump modeling of EEG signals