Biomedical subjects

Arnaud Delorme

Publications and source records attributed to Arnaud Delorme.

11 recordsLinked to original sources

PubMed2006

Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis.

Detecting artifacts produced in EEG data by muscle activity, eye blinks and electrical noise is a common and important problem in EEG research. It is now widely accepted that independent component analysis (ICA) may be a useful tool for isolating artifacts and/or cortical processes from electroencephalographic (EEG) data. We present results of simulations demonstrating that ICA decomposition, here tested using three popular ICA algorithms, Infomax, SOBI, and FastICA, can allow more sensitive automated detection of small non-brain artifacts than applying the same detection methods directly to the scalp channel data. We tested the upper bound performance of five methods for detecting various types of artifacts by separately optimizing and then applying them to artifact-free EEG data into which we had added simulated artifacts of several types, ranging in size from thirty times smaller (-50 dB) to the size of the EEG data themselves (0 dB). Of the methods tested, those involving spectral thresholding were most sensitive. Except for muscle artifact detection where we found no gain of using ICA, all methods proved more sensitive when applied to the ICA-decomposed data than applied to the raw scalp data: the mean performance for ICA was higher and situated at about two standard deviations away from the performance distribution obtained on raw data. We note that ICA decomposition also allows simple subtraction of artifacts accounted for by single independent components, and/or separate and direct examination of the decomposed non-artifact processes themselves.

Algorithms↗

PubMed2005

Frontal midline EEG dynamics during working memory.

We show that during visual working memory, the electroencephalographic (EEG) process producing 5-7 Hz frontal midline theta (fmtheta) activity exhibits multiple spectral modes involving at least three frequency bands and a wide range of amplitudes. The process accounting for the fmtheta increase during working memory was separated from 71-channel data by clustering on time/frequency transforms of components returned by independent component analysis (ICA). Dipole models of fmtheta component scalp maps were consistent with their generation in or near dorsal anterior cingulate cortex. From trial to trial, theta power of fmtheta components varied widely but correlated moderately with theta power in other frontal and left temporal processes. The weak mean increase in frontal midline theta power with increasing memory load, produced entirely by the fmtheta components, largely reflected progressively stronger theta activity in a relatively small proportion of trials. During presentations of letter series to be memorized or ignored, fmtheta components also exhibited 12-15 Hz low-beta activity that was stronger during memorized than during ignored letter trials, independent of letter duration. The same components produced a brief 3-Hz burst 500 ms after onset of the Probe letter following each letter sequence. A new decomposition method, log spectral ICA, applied to normalized log time/frequency transforms of fmtheta component Memorize-letter trials, showed that their low-beta activity reflected harmonic energy in continuous, sharp-peaked theta wave trains as well as independent low-beta bursts. Possibly, the observed fmtheta process variability may index dynamic adjustments in medial frontal cortex to trial-specific behavioral context and task demands.

Adult↗

PubMed2005

High-frequency gamma-band activity in the basal temporal cortex during picture-naming and lexical-decision tasks.

Gamma-band activity (GBA) in electroencephalograms (EEGs) has been shown to reflect various cognitive processes. GBA has typically been recorded in the 30-60 Hz range in scalp EEGs. Recently, task-related "high GBA" (HGBA) with frequencies up to 100 Hz has been observed in studies with invasive electrocorticograms (ECoGs). In the present study, we recorded ECoGs from the bilateral basal temporal cortices in a patient with epilepsy and evaluated the task-related HGBA (most prominently in the 80-120 Hz range) accompanying picture-naming and lexical-decision tasks. We examined picture naming using two categories (line drawings of animals and tools). The lexical-decision task was performed using words and pseudowords of two distinct Japanese writing forms, kanji (morphograms) and kana (syllabograms). Task-related HGBA was observed bilaterally during the naming task. Recordings from some electrodes revealed significant differences in HGBA between animal and tool pictures. In contrast to the naming task, there was apparent left dominance in the lexical-decision task. Furthermore, significant differences in HGBA were observed between the Japanese kanji and kana words and between the kanji words and kanji pseudowords. A number of differences in the HGBA observed in the recordings from the basal temporal area were consistent with previous findings from neuroimaging and patient studies and suggest that HGBA is a good correlate of visual cognitive functions.

Acoustic Stimulation↗

PubMed2005

Rapid categorization of natural scenes in monkeys: target predictability and processing speed.

Three monkeys performed a categorization task and a recognition task with briefly flashed natural images, using in alternation either a large variety of familiar target images (animal or food) or a single (totally predictable) target. The processing time was 20 ms shorter in the recognition task in which false alarms showed that monkeys relied on low-level cues (color, form, orientation, etc.). The 20-ms additional delay necessary in monkeys to perform the categorization task is compared with the 40-ms delay previously found for humans performing similar tasks. With such short additional processing time, it is argued that neither monkeys nor humans have time to develop a fully integrated object representation in the categorization task and must rely on coarse intermediate representations.

Animals↗

PubMed2005

What is novel in the novelty oddball paradigm? Functional significance of the novelty P3 event-related potential as revealed by independent component analysis.

To better understand whether voluntary attention affects how the brain processes novel events, variants of the auditory novelty oddball paradigm were presented to two different groups of human volunteers. One group of subjects (n=16) silently counted rarely presented 'infrequent' tones (p=0.10), interspersed with 'novel' task-irrelevant unique environmental sounds (p=0.10) and frequently presented 'standard' tones (p=0.80). A second group of subjects (n=17) silently counted the 'novel' environmental sounds, the 'infrequent' tones now serving as the task-irrelevant deviant events. Analysis of event-related potentials (ERPs) recorded from 63 scalp channels suggested a spatiotemporal overlap of fronto-central novelty P3 and centro-parietal P3 (P3b) ERP features in both groups. Application of independent component analysis (ICA) to concatenated single trials revealed two independent component clusters that accounted for portions of the novelty P3 and P3b response features, respectively. The P3b-related ICA cluster contributed to the novelty P3 amplitude response to novel environmental sounds. In contrast to the scalp ERPs, the amplitude of the novelty P3 related cluster was not affected by voluntary attention, that is, by the target/nontarget distinction. This result demonstrates the usefulness of ICA for disentangling spatiotemporally overlapping ERP processes and provides evidence that task irrelevance is not a necessary feature of novelty processing.

Acoustic Stimulation↗

PubMed2004

Electroencephalographic brain dynamics following manually responded visual targets.

Scalp-recorded electroencephalographic (EEG) signals produced by partial synchronization of cortical field activity mix locally synchronous electrical activities of many cortical areas. Analysis of event-related EEG signals typically assumes that poststimulus potentials emerge out of a flat baseline. Signals associated with a particular type of cognitive event are then assessed by averaging data from each scalp channel across trials, producing averaged event-related potentials (ERPs). ERP averaging, however, filters out much of the information about cortical dynamics available in the unaveraged data trials. Here, we studied the dynamics of cortical electrical activity while subjects detected and manually responded to visual targets, viewing signals retained in ERP averages not as responses of an otherwise silent system but as resulting from event-related alterations in ongoing EEG processes. We applied infomax independent component analysis to parse the dynamics of the unaveraged 31-channel EEG signals into maximally independent processes, then clustered the resulting processes across subjects by similarities in their scalp maps and activity power spectra, identifying nine classes of EEG processes with distinct spatial distributions and event-related dynamics. Coupled two-cycle postmotor theta bursts followed button presses in frontal midline and somatomotor clusters, while the broad postmotor "P300" positivity summed distinct contributions from several classes of frontal, parietal, and occipital processes. The observed event-related changes in local field activities, within and between cortical areas, may serve to modulate the strength of spike-based communication between cortical areas to update attention, expectancy, memory, and motor preparation during and after target recognition and speeded responding.

Adult↗

PubMed2004

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

We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.

Computer Simulation↗

PubMed2004

Interaction of top-down and bottom-up processing in the fast visual analysis of natural scenes.

The influence of task requirements on the fast visual processing of natural scenes was studied in 14 human subjects performing in alternation an "animal" categorization task and a single-photograph recognition task. Target photographs were randomly mixed with non-target images and flashed for only 20 ms. Subjects had to respond to targets within 1 s. Processing time for image-recognition was 30-40 ms shorter than for the categorization task, both for the fastest behavioral responses and for the latency at which event related potentials evoked by target and non-target stimuli started to diverge. The faster processing in image-recognition is shown to be due to the use of low-level cues, but source analysis produced evidence that, regardless of the task, the dipoles accounting for the differential activity had the same localization and orientation in the occipito-temporal cortex. We suggest that both tasks involve the same visual pathway and the same decisional brain area but because of the total predictability of the target in the image recognition task, the first wave of bottom-up feed-forward information is speeded up by top-down influences that might originate in the prefrontal cortex and preset lower levels of the visual pathway to the known target features.

Adult↗

PubMed2004

Mining event-related brain dynamics.

This article provides a new, more comprehensive view of event-related brain dynamics founded on an information-based approach to modeling electroencephalographic (EEG) dynamics. Most EEG research focuses either on peaks 'evoked' in average event-related potentials (ERPs) or on changes 'induced' in the EEG power spectrum by experimental events. Although these measures are nearly complementary, they do not fully model the event-related dynamics in the data, and cannot isolate the signals of the contributing cortical areas. We propose that many ERPs and other EEG features are better viewed as time/frequency perturbations of underlying field potential processes. The new approach combines independent component analysis (ICA), time/frequency analysis, and trial-by-trial visualization that measures EEG source dynamics without requiring an explicit head model.

Action Potentials↗

PubMed2003

EEG changes accompanying learned regulation of 12-Hz EEG activity.

We analyzed 15 sessions of 64-channel electroencephalographic (EEG) data recorded from a highly trained subject during sessions in which he attempted to regulate power at 12 Hz over his left- and right-central scalp to control the altitude of a cursor moving toward target boxes placed at the top-, middle-, or bottom-right of a computer screen. We used infomax independent component analysis (ICA) to decompose 64-channel EEG data from trials in which the subject successfully up- or down-regulated the measured EEG signals. Applying time-frequency analysis to the time courses of activity of several of the resulting 64 independent EEG components revealed that successful regulation of the measured activity was accompanied by extensive, asymmetrical changes in power and coherence, at both nearby and distant frequencies, in several parts of cortex. A more complete understanding of these phenomena could help to explain the nature and locus of learned regulation of EEG rhythms and might also suggest ways to further optimize the performance of brain-computer interfaces.

Adaptation, Physiological↗

PubMed2003

SpikeNET: an event-driven simulation package for modelling large networks of spiking neurons.

Many biological neural network models face the problem of scalability because of the limited computational power of today's computers. Thus, it is difficult to assess the efficiency of these models to solve complex problems such as image processing. Here, we describe how this problem can be tackled using event-driven computation. Only the neurons that emit a discharge are processed and, as long as the average spike discharge rate is low, millions of neurons and billions of connections can be modelled. We describe the underlying computation and implementation of such a mechanism in SpikeNET, our neural network simulation package. The type of model one can build is not only biologically compliant, it is also computationally efficient as 400 000 synaptic weights can be propagated per second on a standard desktop computer. In addition, for large networks, we can set very small time steps (< 0.01 ms) without significantly increasing the computation time. As an example, this method is applied to solve complex cognitive tasks such as face recognition in natural images.

Action Potentials↗