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.

Amplitude of evoked potentials and degree of event-related desynchronization (ERD) during photic stimulation.

The linear estimation (LE) and spline surface Laplacian (SL) method were applied to single-trial EEG data. EEG was recorded in three subjects during voluntary, self-paced movements of the index finger. The EEG data were bandpass-filtered in the lower beta frequency range and showed short-lasting bursts of oscillations after termination of movement. These oscillations are termed postmovement beta synchronization. The realistic head geometry and the digitized positions of the electrodes were taken into account for accurate modeling of the anatomy. Regularization of the LE method was achieved by the truncated singular value decomposition. The LE and SL distribution of the postmovement beta synchronization showed similar spatial and temporal patterns. A clear increase of the LE source activity was found over the primary motor area. These results indicate that the postmovement beta synchronization is generated over the anterior bank of the central sulcus.

Functional imaging of postmovement beta event-related synchronization.

Low-frequency oscillations with a dominant frequency at 0.1 Hz are one of the most influential intrinsic blood-oxygen-level-dependent (BOLD) signals. This raises the question if vascular BOLD oscillations (originating from blood flow in the brain) and intrinsic slow neural activity fluctuations (neural BOLD oscillations) can be differentiated. In this study, we report on two different approaches: first, on computing the phase-locking value in the frequency band 0.07-0.13 Hz between heart beat-to-beat interval (RRI) and BOLD oscillations and second, between multiple BOLD oscillations (functional connectivity) in four resting states in 23 scanner-naive, anxious healthy subjects. The first method revealed that vascular 0.1-Hz BOLD oscillations preceded those in RRI signals by 1.7 ± 0.6 s and neural BOLD oscillations lagged RRI oscillations by 0.8 ± 0.5 s. Together, vascular BOLD oscillations preceded neural BOLD oscillations by ~90° or ~2.5 s. To verify this discrimination, connectivity patterns of neural and vascular 0.1-Hz BOLD oscillations were compared in 26 regions involved in processing of emotions. Neural BOLD oscillations revealed significant phase-coupling between amygdala and medial frontal cortex, while vascular BOLD oscillations showed highly significant phase-coupling between amygdala and multiple regions in the supply areas of the anterior and medial cerebral arteries. This suggests that not only slow neural and vascular BOLD oscillations can be dissociated but also that two strategies may exist to optimize regulation of anxiety, that is increased functional connectivity between amygdala and medial frontal cortex, and increased cerebral blood flow in amygdala and related structures.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/ejn.13845

Synchronization of intrinsic 0.1‐Hz blood‐oxygen‐level‐dependent oscillations in amygdala and prefrontal cortex in subjects with increased state anxiety

Electroencephalograph (EEG) recordings during right and left hand motor imagery can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communication channel to replace an impaired motor function. It can be used by e.g., handicap users with amyotrophic lateral sclerosis (ALS). The conventional method purposes the recognition of right hand and left hand motor imagery. In this paper, feature extraction based on self organizing maps (SOM) using auto-regressive (AR) spectrum was introduced to discriminate the EEG signals recorded during right hand, left hand and foot motor imagery. The features in pattern recognition are discussed through the experimental studies.

Pattern Recognition of EEG Signal during Motor Imagery by Using SOM

One characteristic feature of the brain is its ability to generate rhythmic potentials or oscillatory activity. Already in 1949 Jasper and Penfield discovered this fact and discussed the relationship between alpha and beta rhythms and their functioning in relation to underlying neural networks. The frequency of brain oscillations depends both on membrane properties of single neurons and the organization and interconnectivity of networks to which they belong (Lopes da Silva, 1991). Such a network can either comprise a large number of neurons controlled, for example, by thalamo-cortical feedback loops or only a small number of neurons interconnected, for example, by intra-cortical feedback loops. Coherent activity in large neuronal pools can result in high amplitude and low frequency oscillations (e. g. alpha band rhythms), whereas synchrony in localized neuronal pools can be the source of gamma oscillations (Lopes da Silva and Pfortscheller, 1999). The dynamic of such a network can result in phasic changes in the synchrony of cell populations due to externally or internally paced events and lead to characteristic EEG patterns. Two such pattern types are observed, the event-related desynchronization, or ERD, in form of an amplitude attenuation and the event-related synchronization, or ERS, in form of an enhancement of specific frequency components (Pfortscheller and Lopes da Silva, 1999a, Pfortscheller and Lopes da Silva, 1999b).

Gert Pfurtscheller

Papers

Amplitude of evoked potentials and degree of event-related desynchronization (ERD) during photic stimulation.

Functional imaging of postmovement beta event-related synchronization.

Synchronization of intrinsic 0.1‐Hz blood‐oxygen‐level‐dependent oscillations in amygdala and prefrontal cortex in subjects with increased state anxiety

Pattern Recognition of EEG Signal during Motor Imagery by Using SOM

Movement and ERD/ERS