Electroencephalography

Common and specific genetic influences on EEG power bands delta, theta, alpha, and beta

by Brendan Zietsch

Recurrent neural processing and somatosensory awareness

by Ryszard Auksztulewicz

Co-authored with Bernhard Spitzer and Felix Blankenburg. Forthcoming in Journal of Neuroscience.

The neural mechanisms of stimulus detection, despite extensive research, remain elusive. The recurrent processing... more The neural mechanisms of stimulus detection, despite extensive research, remain elusive. The recurrent processing hypothesis, a prominent theoretical account of perceptual awareness, states that while stimuli might in principle evoke feedforward activity propagating through the visual cortex, stimuli which become consciously detected are further processed in feedforward-feedback loops established between cortical areas. To test this theory in the tactile modality, we applied dynamic causal modeling (DCM) to electroencephalography (EEG) data acquired from humans in a somatosensory detection task. In the analysis of stimulation-induced event related potentials (ERPs), we focused on model-based evidence for feedforward, feedback and recurrent processing between primary and secondary somatosensory cortices. Bayesian model comparison revealed that while early EEG components were well explained by both the feedforward and the recurrent models, the recurrent model outperformed the other models when later EEG segments were analyzed. Within the recurrent model, stimulus detection was characterized by a relatively early strength increase of the feedforward connection from primary to secondary somatosensory cortex (> 80 ms). At longer latencies (> 140 ms), also the feedback connection showed a detection-related strength increase. The modeling results on relative evidence between recurrent and feedforward model comparison support the hypothesis that the ERP responses from sensory areas arising after aware stimulus detection can be explained by increased recurrent processing within the somatosensory network in the later stages of stimulus processing.

Tagging the Neuronal Entrainment to Beat and Meter

by André Mouraux

Nozaradan S, Peretz I, Missal M, Mouraux A. J Neurosci (2011); 31(28): 10234-40.

Feeling the beat and meter is fundamental to the experience of music. However, how these periodicities are represented... more Feeling the beat and meter is fundamental to the experience of music. However, how these periodicities are represented in the brain remains largely unknown. Here, we test whether this function emerges from the entrainment of neurons resonating to the beat and meter. We recorded the electroencephalogram while participants listened to a musical beat and imagined a binary or a ternary meter on this beat (i.e., a march or a waltz). We found that the beat elicits a sustained periodic EEG response tuned to the beat frequency. Most importantly, we found that meter imagery elicits an additional frequency tuned to the corresponding metric interpretation of this beat. These results provide compelling evidence that neural entrainment to beat and meter can be captured directly in the electroencephalogram. More generally, our results suggest that music constitutes a unique context to explore entrainment phenomena in dynamic cognitive processing at the level of neural networks.

FASTER: fully automated statistical thresholding for EEG artifact rejection

by Robert Whelan

FASTER is now a freely available EEGLAB plugin: http://sourceforge.net/projects/faster/

Electroencephalogram (EEG) data are typically contaminated with artifacts (e.g., by eye movements). The effect of... more Electroencephalogram (EEG) data are typically contaminated with artifacts (e.g., by eye movements). The effect of artifacts can be attenuated by deleting data with amplitudes over a certain value, for example. Independent component analysis (ICA) separates EEG data into neural activity and artifact; once identified, artifactual components can be deleted from the data. Often, artifact rejection algorithms require supervision (e.g., training using canonical artifacts). Many artifact rejection methods are time consuming when applied to high-density EEG data. We describe FASTER (Fully Automated Statistical Thresholding for EEG artifact Rejection). Parameters were estimated for various aspects of data (e.g., channel variance) in both the EEG time series and in the independent components of the EEG: outliers were detected and removed. FASTER was tested on both simulated EEG (n = 47) and real EEG (n = 47) data on 128-, 64-, and 32-scalp electrode arrays. FASTER was compared to supervised artifact detection by experts and to a variant of the Statistical Control for Dense Arrays of Sensors (SCADS) method. FASTER had >90% sensitivity and specificity for detection of contaminated channels, eye movement and EMG artifacts, linear trends and white noise. FASTER generally had >60% sensitivity and specificity for detection of contaminated epochs, vs. 0.15% for SCADS. FASTER also aggregates the ERP across subject datasets, and detects outlier datasets. The variance in the ERP baseline, a measure of noise, was significantly lower for FASTER than either the supervised or SCADS methods. ERP amplitude did not differ significantly between FASTER and the supervised approach.

Multiresolution schemes for M/EEG source characterization

by Sylvain Baillet

Generalized time-frequency coherency for assessing neural interactions in electrophysiological recordings

by Tjeerd Boonstra

Mehrkanoon S, Breakspear M, Daffertshofer A, Boonstra TW (2011). Nature Precedings npre.2011.6615.1.

Time-frequency coherence has been widely used to quantify statistical dependencies in bivariate data and has proven to... more Time-frequency coherence has been widely used to quantify statistical dependencies in bivariate data and has proven to be vital for the study of neural interactions in electro- physiological recordings. Conventional methods establish time-frequency coherence by smoothing the cross and power spectra using identical smoothing procedures. Smooth- ing entails a trade-off between time-frequency resolution and statistical consistency and is critical for detecting instantaneous coherence in single-trial data. Here, we propose a generalized method to estimate time-frequency coherency by using different smooth- ing procedures for the cross spectra versus power spectra. This novel method has an improved trade-off between time resolution and statistical consistency compared to con- ventional methods, as verified by two simulated data sets. The methods are then applied to single-trial surface encephalography recorded from human subjects for comparative purposes. Our approach extracted robust alpha- and gamma-band synchronization over the visual cortex that was not detected by conventional methods, demonstrating the efficacy of this method.

Dissociation of early evoked cortical activity in perceptual grouping

by Sergei Gepshtein

Nikolaev, A. R., Gepshtein, S., Kubovy, M., & van Leeuwen, C. (2008).
Dissociation of early evoked cortical activity in perceptual grouping.
Experimental Brain Research, 186 (1), p. 107-122.

Perceptual grouping is a multi-stage process, irreducible to a single mechanism localized anatomically or... more Perceptual grouping is a multi-stage process, irreducible to a single mechanism localized anatomically or chronometrically. To understand how various grouping mechanisms interact, we combined a phenomenological report paradigm with high-density event-related potential (ERP) measurements, using a 256-channel electrode array. We varied the relative salience of competing perceptual organizations in multi-stable dot lattices and asked observers to report perceived groupings. The ability to discriminate groupings (the grouping sensitivity) was positively correlated with the amplitude of the earliest ERP peak C1 (about 60 ms after stimulus onset) over the middle occipital area. This early activity is believed to reflect spontaneous feed-forward processes preceding perceptual awareness. Grouping sensitivity was negatively correlated with the amplitude of the next peak P1 (about 110 ms), which is believed to reflect lateral and feedback interactions associated with perceptual awareness and attention. This dissociation between C1 and P1 activity implies that the recruitment of fast, spontaneous mechanisms for grouping leads to high grouping sensitivity. Observers who fail to recruit these mechanisms are trying to compensate by using later mechanisms, which depend less on stimulus properties such as proximity.

Magnetoencephalography demonstrates multiple asynchronous generators during human sleep spindles

by Nima Dehghani

Comparative power spectral analysis of simultaneous elecroencephalographic and magnetoencephalographic recordings in humans suggests non-resistive …

by Nima Dehghani

Divergent cortical generators of MEG and EEG during human sleep spindles suggested by distributed source modeling

by Nima Dehghani

Emergence of synchronous EEG spindles from asynchronous MEG spindles

by Nima Dehghani

Auditory steady state response in bipolar disorder: relation to clinical state, cognitive performance, medication status, and substance disorders

by Olga Rass

Emotion assessment for affective-computing based on brain and peripheral signals

by Guillaume Chanel

Chanel, G., SciD Thesis, Computer Science Departement, University of Geneva, Geneva, 2009

This thesis deal with the usability of two types of physiological activity for emotion assessment in the context of... more This thesis deal with the usability of two types of physiological activity for emotion assessment in the context of affective computing: the central nervous system (the brain) and the peripheral nervous system activities. The central activity is measured by using eletroencephalograms (EEGs). The peripheral activity is measured with the following sensors: a galvanic skin response sensor (GSR); a respiration belt; a plethysmograph and a skin temperature sensor. The results demonstrates the interest of these information sources and of pattern recognition methods for emotion assessment in the valence-arousal space. Better results were obtained with EEG signals than peripheral signals on short time windows and the fusion of those two types of information enables to increase the recognition accuracy. These results were validated for several protocols and in the context of video game interaction.

Multimodal focus attention and stress detection and feedback in an augmented driver simulator

by Guillaume Chanel

Benoit A., Bonnaud L., Caplier A., Ngo P., Lawson L., Trevisan D. G., Levacic V., Mancas C., Chanel G., Personal and Ubiquitous Computing , 13(1) , 33-41, January 2009

Short-term emotion assessment in a recall paradigm

by Guillaume Chanel

Chanel, G., Kierkels, J. J. M., Soleymani, M.,  Pun, T.,  International Journal of Human-Computer Studies, 67(8), 607-627, 2009.

Emotion Assessment From Physiological Signals for Adaptation of Game Difficulty

by Guillaume Chanel

Chanel, G. ; Rebetez, C. ; Bétrancourt, M. ; Pun, T. . IEEE Transactions on Systems Man and Cybernetics, Part A: Systems and Humans, 41 (6), 1052-1063, 2011

This paper proposes to maintain player's engagement by adapting game difficulty according to player's emotions... more This paper proposes to maintain player's engagement by adapting game difficulty according to player's emotions assessed from physiological signals. The validity of this approach was first tested by analyzing the questionnaire responses, electroencephalogram (EEG) signals, and peripheral signals of the players playing a Tetris game at three difficulty levels. This analysis confirms that the different difficulty levels correspond to distinguishable emotions, and that, playing several times at the same difficulty level gives rise to boredom. The next step was to train several classifiers to automatically detect the three emotional classes from EEG and peripheral signals in a player-independent framework. By using either type of signals, the emotional classes were successfully recovered, with EEG having a better accuracy than peripheral signals on short periods of time. After the fusion of the two signal categories, the accuracy raised up to 63%.

Classification Methods for Ongoing EEG and MEG Signals

by Sylvain Baillet

Added noise affects the neural correlates of upright and inverted faces differently.

by Jordan DeLong

Schneider, B. L., DeLong, J. E., & Busey, T. A. (2007). Added noise affects the neural correlates of upright and inverted faces differently. Journal of Vision, 7(4):4, 1-24, http://journalofvision.org/7/4/4/, doi:10.1167/7.4.4.

In five experiments, we examine the neural correlates of the interaction between upright faces, inverted faces, and... more In five experiments, we examine the neural correlates of the interaction between upright faces, inverted faces, and visual noise. In Experiment 1, we examine a component termed the N170 for upright and inverted faces presented with and without noise. Results show a smaller amplitude for inverted faces than upright faces when presented in noise, whereas the reverse is true without noise. In Experiment 2, we show that the amplitude reversal is robust for full faces but not eyes alone across all noise levels. In Experiment 3, we vary contrast to see if this reversal is a result of degrading a face. We observe no reversal effects. Thus, across conditions, adding noise to full faces is a sufficient condition for the N170 reversal. In Experiment 4, we delay the onsets of the faces presented in noise. We replicate the smaller N170 for inverted faces at no delay but observe partial recovery of the N170 for inverted faces at longer delays in static noise. Experiment 5 demonstrates the interaction in low contrast at a behavioral level. We propose a model in which noise interacts with the processing properties of inverted faces more so than upright faces.

Adaptation modulate the electrophysiological substrates of perceived facial distortion: Support for opponent coding.

by Jordan DeLong

Burkhardt, A., Blaha, L. M., Jurs, B. S., Rhodes, G., Jeffrey, L., Wyatte, D., DeLong, J., Busey, T. (2010) Adaptation modulate the electrophysiological substrates of perceived facial distortion: Support for opponent coding. Neuropsychologia, 48, 3743-3756

In two experiments we determined the electrophysiological substrates of figural aftereffects in face adaptation using... more In two experiments we determined the electrophysiological substrates of figural aftereffects in face adaptation using compressed and expanded faces. In Experiment 1, subjects viewed a series of compressed and expanded faces. Results demonstrated that distortion systematically modulated the peak amplitude of the P250 event-related potential (ERP) component. As the amount of perceived distortion in a face increased, the peak amplitude of the P250 component decreased, regardless of whether the physical distortion was compressive or expansive. This provided an ERP metric of the degree of perceived distortion. In Experiment 2, we examined the effects of adaptation on the P250 amplitude by introducing an adapting stimulus that affected the subject's perception of the distorted test faces as measured through normality judgments. The set of test faces was held constant and the adapting stimulus was systematically varied across experimental days. Adapting to a compressed face made a less compressed test face appear more normal and an expanded test face more distorted as measured by normality ratings. We found that the adaptation conditions that increased the perceived distortion of the distorted test faces also decreased the amplitude of the P250. Likewise, adaptation conditions that decreased the perceived distortion of the distorted test faces also increased the amplitude of the P250. The results demonstrate that perceptual adaptation to compressed or expanded faces affected not only the behavioral normality judgments but also the electrophysiological correlates of face processing in the window of 190–260 ms after stimulus onset.

BRAIN-COMPUTER INTERFACING WITH EEG: A LOOK AT EYE MOVEMENTS

by Jordan DeLong

Unpublished Senior Thesis: May, 2007

This study utilizes Independent Component Analysis to ocular artifacts in order to determine their effects on an EEG... more This study utilizes Independent Component Analysis to ocular artifacts in order to determine their effects on an EEG based Brain‐Computer Interface (BCI). EEG data was recorded while four subjects were visualizing movement to targets on the right and left of the chamber. The BCI was tested and constructed using a combination of Fast Fourier Transform, Linear Discriminant Analysis and Adaptive Boosting. Removing eye blinks increased classification of the system, suggesting that the BCI is not driven by eye movements, but actually hindered by them. This technique could be used in future designs to maximize categorization accuracy