https://sccn.ucsd.edu/eeglab/download/eeglab_jnm03.pdf

EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.

THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

Brain-computer interfaces for communication and control.

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.

Distinction Sensitive Learning Vector Quantization (DSLVQ) application as a classifier based feature selection method for a Brain Computer Interface

Occipital rhythmic activity within the alpha band during conditioned externally paced movement.

Beside standard questionnaires physiological measures can be used to describe the state of Presence in a virtual environment. A total of 21 participants explored a virtual bar in a CAVE like system. The experiment was divided into a baseline-, training- and experimental phase. During the experimental phase breaks in presence (BIPS) in form of whiteouts of the virtual environment scenario were induced. The heart rate variability and the event-related ECG were calculated from the acquired ECG data. The study showed that the heart rate variability can be used as parameter that reflects the physiological state of the participant. The event-related ECG showed the effect of the induced BIPS and of speaking avatars on the subjects.

Heart-Rate Variability and Event-Related ECG in Virtual Environments

Sleep classification in infants by decision tree-based neural networks

Non-linear time sequence analysis has been performed on infant sleep measurement data in order to obtain more information about the respiratory processes. As a first step, respiration data during REM sleep were analysed with methods from non-linear dynamics, especially, the correlation integral and the slope of its log-log plot, representing the correlation dimension. Before calculation of the correlation integral, a special kind of filtering has to be applied to the data. This filtering algorithm is a state space and singular value decomposition-based noise reduction method, and it is used to separate the noise and signal subspaces. The dynamics of a signal (in our case data from the respiratory process) and its degrees of freedom can be characterised by the correlation integral and by the correlation dimension, respectively. The main result of this study is that the highly irregular-looking breathing patterns during REM sleep could be described by a deterministic system, and finally the physiological significance of this finding is discussed.

Gert Pfurtscheller

Papers

Distinction Sensitive Learning Vector Quantization (DSLVQ) application as a classifier based feature selection method for a Brain Computer Interface

Occipital rhythmic activity within the alpha band during conditioned externally paced movement.

Heart-Rate Variability and Event-Related ECG in Virtual Environments

Sleep classification in infants by decision tree-based neural networks

Application of the correlation integral to respiratory data of infants during REM sleep