Baby's Brain

European Data Format (EDF): technical specification

European Data Format (EDF): technical specification: "THE EDF FORMAT "
Full specification of THE EDF FORMAT as described in the article by
Bob Kemp, Alpo V�rri, Agostinho C. Rosa, Kim D. Nielsen and John Gade
"A simple format for exchange of digitized polygraphic recordings"
Electroencephalography and Clinical Neurophysiology, 82 (1992) 391-393.

Recovering dipole sources from scalp-recorded event-related-potentials using component analysis:

Recovering dipole sources from scalp-recorded event-related-potentials using component analysis: principal component analysis and independent
component analysis

Abstract
Principal component analysis (PCA) and independent component analysis (ICA) were examined in their ability to recover dipole sources from simulated data. Datasets of EEG segments were generated that contained cortical sources that were temporally overlapping or non-overlapping, and dipole sources with varying degree of spatial orthogonality. For temporal overlapping sources, both PCA and ICA resulted in components that required multiple-source equivalent current dipole models. The spatially overlapping sources affected the PCA method more than ICA, resulting in single PCA components in which all non-orthogonal sources were represented. For both PCA and ICA, dipole models with fixed-location dipoles successfully accounted for most of the variance in the component weights, even when the spatial or temporal overlap of the generating sources required multiple-dipole models.

BESA | Brain Electrical Source Analysis

BESA | Brain Electrical Source Analysis: "Founded by Dr. Michael Scherg in 1995, MEGIS combines expert know-how in the different fields of neuroscience with latest computational techniques. Results are the famous BESA and FOCUS programs, whose capabilities are appreciated by users in clinical facilities and research institutes all over the world."

Converging Evidence of Linear Independent Components in EEG

Converging Evidence of Linear Independent Components in EEG

Abstract—Blind source separation (BSS) has been proposed as a method to analyze multi-channel electroencephalography
(EEG) data. A basic issue in applying BSS algorithms is the validity of the independence assumption. In this paper we
investigate whether EEG can be considered to be a linear combination of independent sources. Linear BSS can be obtained
under the assumptions of non-Gaussian, non-stationary, or non-white independent sources. If the linear independence
hypothesis is violated these three different conditions will not necessarily lead to the same result. We show, using 64 channel
EEG data, that different algorithms which incorporate the three different assumptions lead to the same results, thus
supporting the linear independence hypothesis.

Blind signal separation

Blind signal separation: "Blind Channel estimation

Blind Signal Separation (BSS) is a recurrent problem in multi-sensors applications where observed signals can be modelled as convolutive or memoryless linear mixtures of unknown source signals. The aim of BSS is to retrieve source signals from observations by exploiting the available a priori information on the pdf of the sources. Real-time applications call for BSS methods that are computationally affordable, fast convergent, stable and reasonably accurate."

Paris' ICA & BSS page

Paris' ICA & BSS page: "Paris' Independent Component Analysis & Blind Source Separation page

Independent Component Analysis (ICA) and Blind Source Separation (BSS) have received quite a lot of attention lately so that I decided to compile a list of online resources for whoever is interested. By no means is this page complete and if you have any additions do send me mail at paris at media dot mit dot edu. In the papers section I do not list all of the papers of every author (that's why you should check their homepages) but the really good ones are here. Also not all ICA & BSS people have home pages so if you discover any or if I missed yours tell me about it and I'll add them.
"

BLIND SEPARATION OF SECOND-ORDER NONSTATIONARY AND TEMPORALLY COLORED SOURCES

BLIND SEPARATION OF SECOND-ORDER NONSTATIONARY AND TEMPORALLY COLORED SOURCES

ABSTRACT
This paper presents a method of blind source separation that jointly exploits the nonstationarity and temporal structure of sources . The method needs only multiple time-delayed correlation matrices of the observation data, each of which is evaluated at different time-windowed data frame, to estimate the demixing matrix. We show that the method is quite robust with respect to the spatially corre-lated but temporally white noise. We also discuss the extension of some existing second-order blind source separation methods. Extensive numerical experiments confirm the validity of the proposed method.

EEGLAB Tutorial: Table of Contents

EEGLAB Tutorial: Table of Contents

free EEG ,ERP data publicly available

free EEG data freely ERP data publicly available EEG data publically available ERP data: "Here is a collection of 32-channel data from 14 subjects (7 males, 7 females) acquired using the Neuroscan software. Subjects are performing a go-nogo categorization task and a go-no recognition task on natural photographs presented very briefly (20 ms). Each subject responded to a total of 2500 trials. Data is CZ referenced and is sampled at 1000 Hz (more details below)."

Cal Tech EEG Database

Cal Tech EEG Database

EEG Database

EEG Database

Neurology and Clinical Neurophysiology

Neurology and Clinical Neurophysiology

98 Event-related brain potentials in the study of visual selective attention

Event-related brain potentials in the study of visual selective attention
STEVEN A. HILLYARD* AND LOURDES ANLLO-VENTO

ABSTRACT Event-related brain potentials (ERPs) pro-vide
high-resolution measures of the time course of neuronal
activity patterns associated with perceptual and cognitive
processes. New techniques for ERP source analysis and com-parisons
with data from blood-flow neuroimaging studies
enable improved localization of cortical activity during visual
selective attention. ERP modulations during spatial attention
point toward a mechanism of gain control over information
f low in extrastriate visual cortical pathways, starting about 80
ms after stimulus onset. Paying attention to nonspatial fea-tures
such as color, motion, or shape is manifested by qual-itatively
different ERP patterns in multiple cortical areas that
begin with latencies of 100–150 ms. The processing of non-spatial
features seems to be contingent upon the prior selec-tion
of location, consistent with early selection theories of
attention and with the hypothesis that spatial attention is
‘‘special.’’

Retinotopic Organization of Early Visual Spatial Attention Effects as Revealed by PET and ERPs

Retinotopic Organization of Early Visual Spatial Attention Effects as Revealed by PET and ERPs
Retinotopic Organization of Early Visual Spatial
Attention Effects as Revealed by PET and ERPs
M.G. Woldorff,* P.T. Fox, M. Matzke, J.L. Lancaster, S. Veeraswamy,
F. Zamarripa, M. Seabolt, T. Glass, J.H. Gao, C.C. Martin, and P. Jerabek
Research Imaging Center, UTHSCSA, San Antonio, Texas 78284-6240
r r
Abstract: Cerebral blood flow PET scans and high-density event-related potentials (ERPs) were recorded
(separate sessions) while subjects viewed rapidly-presented, lower-visual-field, bilateral stimuli. Active
attention to a designated side of the stimuli (relative to passive-viewing conditions) resulted in an
enhanced ERP positivity (P1 effect) from 80–150 msec over occipital scalp areas contralateral to the
direction of attention. In PET scans, active attention vs. passive showed strong activation in the
contralateral dorsal occipital cortex, thus following the retinotopic organization of the early extrastriate
visual sensory areas, with some weaker activation in the contralateral fusiform. Dipole modeling seeded
by the dorsal occipital PET foci yielded an excellent fit for the P1 attention effect. In contrast, dipoles
constrained to the fusiform foci fit the P1 effect poorly, and, when the location constraints were released,
moved upward to the dorsal occipital locations during iterative dipole fitting. These results argue that the
early ERP P1 attention effects for lower-visual-field stimuli arise mainly from these dorsal occipital areas
and thus also follow the retinotopic organization of the visual sensory input pathways. These combined
PET/ERP data therefore provide strong evidence that sustained visual spatial attention results in a preset,
top-down biasing of the early sensory input channels in a retinotopically organized way. Hum. Brain
Mapping 5:280–286, 1997. r 1997 Wiley-Liss, Inc.