Pre-stimulus spectral EEG patterns and the visual evoked response

The relationship between the latencies and amplitudes of the N1 and P2 components of the visual evoked potential (VEP) and the psychophysiological state of the brain immediately preceding the time of the stimulus has been investigated in 7 male subjects. Power spectral measures in the delta, theta, alpha and beta bands of the 1 sec pre-stimulus EEG were used to assess the brain state, and low intensity flashes, delivered randomly between 2 and 6 whole seconds, were used as the stimuli. Trials were ranked separately according to the relative amounts of pre-stimulus power in each EEG band and were partitioned into groups by an equal pre-stimulus spectral power criterion. Averaged EPs were computed from these groups and multiple regression analysis was used to relate pre-stimulus spectral power values to EP features. Five of the 7 subjects displayed consistent increases in N1-P2 amplitude as a function of increasing pre-stimulus relative alpha power. The between-subjects effect of pre-stimulus EEG on N1 latency was small, but was moderate for P2 latency (both significant). Both N1 and P2 latency were found to decrease with increasing amounts of pre-stimulus relative delta and theta power.     

Hemispheric Specialization Varies with EEG Brain Resting States and Phase of Menstrual Cycle

A growing body of behavioral studies has demonstrated that women’s hemispheric specialization varies as a function of their menstrual cycle, with hemispheric specialization enhanced during their menstruation period. Our recent high-density electroencephalogram (EEG) study with lateralized emotional versus neutral words extended these behavioral results by showing that hemispheric specialization in men, but not in women under birth-control, depends upon specific EEG resting brain states at stimulus arrival, suggesting that hemispheric specialization may be pre-determined at the moment of the stimulus onset. To investigate whether EEG brain resting state for hemispheric specialization could vary as a function of the menstrual phase, we tested 12 right-handed healthy women over different phases of their menstrual cycle combining high-density EEG recordings and the same lateralized lexical decision paradigm with emotional versus neutral words. Results showed the presence of specific EEG resting brain states, associated with hemispheric specialization for emotional words, at the moment of the stimulus onset during the menstruation period only. These results suggest that the pre-stimulus EEG pattern influencing hemispheric specialization is modulated by the hormonal state.     

Recognition of cognitive load with a stacking network ensemble of denoising autoencoders and abstracted neurophysiological features

The safety of human–machine systems can be indirectly evaluated based on operator’s cognitive load levels at each temporal instant. However, relevant features of cognitive states are hidden behind in multiple sources of cortical neural responses. In this study, we developed a novel neural network ensemble, SE-SDAE, based on stacked denoising autoencoders (SDAEs) which identify different levels of cognitive load by electroencephalography (EEG) signals. To improve the generalization capability of the ensemble framework, a stacking-based approach is adopted to fuse the abstracted EEG features from activations of deep-structured hidden layers. In particular, we also combine multiple K-nearest neighbor and naive Bayesian classifiers with SDAEs to generate a heterogeneous classification committee to enhance ensemble’s diversity. Finally, we validate the proposed SE-SDAE by comparing its performance with mainstream pattern classifiers for cognitive load evaluation to show its effectiveness.     

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

Transcranial Magnetic Stimulation (TMS) is an effective method for establishing a causal link between a cortical area and cognitive/neurophysiological effects. Specifically, by creating a transient interference with the normal activity of a target region and measuring changes in an electrophysiological signal, we can establish a causal link between the stimulated brain area or network and the electrophysiological signal that we record. If target brain areas are functionally defined with prior fMRI scan, TMS could be used to link the fMRI activations with evoked potentials recorded. However, conducting such experiments presents significant technical challenges given the high amplitude artifacts introduced into the EEG signal by the magnetic pulse, and the difficulty to successfully target areas that were functionally defined by fMRI. Here we describe a methodology for combining these three common tools: TMS, EEG, and fMRI. We explain how to guide the stimulator''s coil to the desired target area using anatomical or functional MRI data, how to record EEG during concurrent TMS, how to design an ERP study suitable for EEG-TMS combination and how to extract reliable ERP from the recorded data. We will provide representative results from a previously published study, in which fMRI-guided TMS was used concurrently with EEG to show that the face-selective N1 and the body-selective N1 component of the ERP are associated with distinct neural networks in extrastriate cortex. This method allows us to combine the high spatial resolution of fMRI with the high temporal resolution of TMS and EEG and therefore obtain a comprehensive understanding of the neural basis of various cognitive processes.     

脑电信息处理

脑电信息处理是脑功能研究重要组成部分。本文介绍了脑电信息处理的前沿领域,包括诱发电位、事件相关电位（ＥＲＰ）、正弦调制光（声）诱发脑电、４０ＨｚＥＲＰ和脑电非线笥动力学研究,并论及了认知活动与分形维数的关系。     

Automatic detection of epileptic EEG signals using higher order cumulant features

The unpredictability of the occurrence of epileptic seizures makes it difficult to detect and treat this condition effectively. An automatic system that characterizes epileptic activities in EEG signals would allow patients or the people near them to take appropriate precautions, would allow clinicians to better manage the condition, and could provide more insight into these phenomena thereby revealing important clinical information. Various methods have been proposed to detect epileptic activity in EEG recordings. Because of the nonlinear and dynamic nature of EEG signals, the use of nonlinear Higher Order Spectra (HOS) features is a seemingly promising approach. This paper presents the methodology employed to extract HOS features (specifically, cumulants) from normal, interictal, and epileptic EEG segments and to use significant features in classifiers for the detection of these three classes. In this work, 300 sets of EEG data belonging to the three classes were used for feature extraction and classifier development and evaluation. The results show that the HOS based measures have unique ranges for the different classes with high confidence level (p-value < 0.0001). On evaluating several classifiers with the significant features, it was observed that the Support Vector Machine (SVM) presented a high detection accuracy of 98.5% thereby establishing the possibility of effective EEG segment classification using the proposed technique.     

A Generalized Framework for Quantifying the Dynamics of EEG Event-Related Desynchronization

Brains were built by evolution to react swiftly to environmental challenges. Thus, sensory stimuli must be processed ad hoc, i.e., independent—to a large extent—from the momentary brain state incidentally prevailing during stimulus occurrence. Accordingly, computational neuroscience strives to model the robust processing of stimuli in the presence of dynamical cortical states. A pivotal feature of ongoing brain activity is the regional predominance of EEG eigenrhythms, such as the occipital alpha or the pericentral mu rhythm, both peaking spectrally at 10 Hz. Here, we establish a novel generalized concept to measure event-related desynchronization (ERD), which allows one to model neural oscillatory dynamics also in the presence of dynamical cortical states. Specifically, we demonstrate that a somatosensory stimulus causes a stereotypic sequence of first an ERD and then an ensuing amplitude overshoot (event-related synchronization), which at a dynamical cortical state becomes evident only if the natural relaxation dynamics of unperturbed EEG rhythms is utilized as reference dynamics. Moreover, this computational approach also encompasses the more general notion of a “conditional ERD,” through which candidate explanatory variables can be scrutinized with regard to their possible impact on a particular oscillatory dynamics under study. Thus, the generalized ERD represents a powerful novel analysis tool for extending our understanding of inter-trial variability of evoked responses and therefore the robust processing of environmental stimuli.     

Classifying four-category visual objects using multiple ERP components in single-trial ERP

Object categorization using single-trial electroencephalography (EEG) data measured while participants view images has been studied intensively. In previous studies, multiple event-related potential (ERP) components (e.g., P1, N1, P2, and P3) were used to improve the performance of object categorization of visual stimuli. In this study, we introduce a novel method that uses multiple-kernel support vector machine to fuse multiple ERP component features. We investigate whether fusing the potential complementary information of different ERP components (e.g., P1, N1, P2a, and P2b) can improve the performance of four-category visual object classification in single-trial EEGs. We also compare the classification accuracy of different ERP component fusion methods. Our experimental results indicate that the classification accuracy increases through multiple ERP fusion. Additional comparative analyses indicate that the multiple-kernel fusion method can achieve a mean classification accuracy higher than 72 %, which is substantially better than that achieved with any single ERP component feature (55.07 % for the best single ERP component, N1). We compare the classification results with those of other fusion methods and determine that the accuracy of the multiple-kernel fusion method is 5.47, 4.06, and 16.90 % higher than those of feature concatenation, feature extraction, and decision fusion, respectively. Our study shows that our multiple-kernel fusion method outperforms other fusion methods and thus provides a means to improve the classification performance of single-trial ERPs in brain–computer interface research.     

Improving spatial and temporal resolution in evoked EEG responses using surface Laplacians

Spline generated surface Laplacian temporal wave forms are presented as a method to improve both spatial and temporal resolution of evoked EEG responses. Middle latency and the N1 components of the auditory evoked response were used to compare potential-based methods with surface Laplacian methods in the time domain. Results indicate that surface Laplacians provide better estimates of underlying cortical activity than do potential wave forms. Spatial discrimination among electrode sites was markedly better with surface Laplacian than with potential wave forms. Differences in the number and latencies of peaks, and their topographic distributions, were observed for surface Laplacian, particularly during the time period encompassing the middle latency responses. Focal activities were observed in surface Laplacian wave forms and topographic maps which were in agreement with previous findings from auditory evoked response studies. Methodological issues surrounding the application of spline methods to the time domain are also discussed. Surface Laplacian methods in the time domain appear to provide an improved way for studying evoked EEG responses by increasing temporal and spatial resolution of component characteristics.     

Application of recurrence quantification analysis for the automated identification of epileptic EEG signals

Epilepsy is a common neurological disorder that is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals are widely used to diagnose seizures. Because of the non-linear and dynamic nature of the EEG signals, it is difficult to effectively decipher the subtle changes in these signals by visual inspection and by using linear techniques. Therefore, non-linear methods are being researched to analyze the EEG signals. In this work, we use the recorded EEG signals in Recurrence Plots (RP), and extract Recurrence Quantification Analysis (RQA) parameters from the RP in order to classify the EEG signals into normal, ictal, and interictal classes. Recurrence Plot (RP) is a graph that shows all the times at which a state of the dynamical system recurs. Studies have reported significantly different RQA parameters for the three classes. However, more studies are needed to develop classifiers that use these promising features and present good classification accuracy in differentiating the three types of EEG segments. Therefore, in this work, we have used ten RQA parameters to quantify the important features in the EEG signals.These features were fed to seven different classifiers: Support vector machine (SVM), Gaussian Mixture Model (GMM), Fuzzy Sugeno Classifier, K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), Decision Tree (DT), and Radial Basis Probabilistic Neural Network (RBPNN). Our results show that the SVM classifier was able to identify the EEG class with an average efficiency of 95.6%, sensitivity and specificity of 98.9% and 97.8%, respectively.     

EEG gamma-band activity during audiovisual speech comprehension in different noise environments

The presence of cross-modal stochastic resonance in different noise environments has been proved in previous behavioral and event-related potential studies, while it was still unclear whether the gamma-band oscillation study was another evidence of cross-modal stochastic resonance. The multisensory gain of gamma-band activity between the audiovisual (AV) and auditory-only conditions in different noise environments was analyzed. Videos of face motion articulating words concordant with different levels of pink noise were used as stimuli. Signal-to-noise ratios (SNRs) of 0, −4, −8, −12 and −16 dB were selected to measure the speech recognition accuracy and EEG activity for 20 healthy subjects. The power and phase of EEG gamma-band oscillations increased in a time window of 50–90 ms. The multisensory gains of evoked and total activity, as well as phase-locking factor, were greatest at the −12 dB SNR, which were consistent with the behavioral result. The multisensory gain of gamma-band activity showed an inverted U-shaped curve as a function of SNR. This finding confirmed the presence of cross-modal stochastic resonance. In addition, there was a significant correlation between evoked activity and phase-locking factor of gamma-band at five different SNRs. Gamma-band oscillation was believed to play a role in the rapid processing and information linkage strengthening of AV modalities in the early stage of cognitive processes.     

Prestimulus EEG influence on late ERP components

Ten healthy volunteers were submitted to an auditory oddball event related potentials (ERP) paradigm. Single trial 500 ms poststimulus ERPs (Pz, Cz, Fz--linked earlobes) along with the correspondent 1000 ms prestimulus EEG (O1-Cz) were stored. EEG epochs were submitted to spectral analysis and a slow wave index (SWI = delta + theta/total) was computed. Three selective ERP averages corresponding to low, medium and high SWI were computed. N2 latency was longer and P3a amplitude was lower in high SWI averages as compared to low SWI averages.     

Electrophysiological Responses to Alcohol Cues Are Not Associated with Pavlovian-to-Instrumental Transfer in Social Drinkers

Pavlovian to Instrumental Transfer (PIT) refers to the behavioral phenomenon of increased instrumental responding for a reinforcer when in the presence of Pavlovian conditioned stimuli that were separately paired with that reinforcer. PIT effects may play an important role in substance use disorders, but little is known about the brain mechanisms that underlie these effects in alcohol consumers. We report behavioral and electroencephalographic (EEG) data from a group of social drinkers (n = 31) who performed a PIT task in which they chose between two instrumental responses in pursuit of beer and chocolate reinforcers while their EEG reactivity to beer, chocolate and neutral pictorial cues was recorded. We examined two markers of the motivational salience of the pictures: the P300 and slow wave event-related potentials (ERPs). Results demonstrated a behavioral PIT effect: responding for beer was increased when a beer picture was presented. Analyses of ERP amplitudes demonstrated significantly larger slow potentials evoked by beer cues at various electrode clusters. Contrary to hypotheses, there were no significant correlations between behavioral PIT effects, electrophysiological reactivity to the cues, and individual differences in drinking behaviour. Our findings are the first to demonstrate a PIT effect for beer, accompanied by increased slow potentials in response to beer cues, in social drinkers. The lack of relationship between behavioral and EEG measures, and between these measures and individual differences in drinking behaviour may be attributed to methodological features of the PIT task and to characteristics of our sample.     

Replicability of MEG and EEG measures of the auditory N1/N1m-response

We investigated the replicability of the source location, amplitude and latency measures of the auditory evoked N1 (EEG) and N1m (MEG) responses. Each of the 5 subjects was measured 6 times in two recording sessions. Responses to monaural stimuli were recorded from 122 MEG and 64 EEG channels simultaneously. The EEG data were modeled with a symmetrically-located dipole pair. For the MEG data, one dipole in each hemisphere was located independently using a subset of channels. Standard deviation (SD) was used as a measure for replicability. The average SD of the x, y and z coordinates of the contralateral N1m dipole was about 2 mm, whereas the corresponding figures for the ipsilateral N1m and the contra- and ipsilateral N1 were about twice as large. The SDs of the dipole amplitudes and latencies were almost equal with MEG and EEG. The amplitude and latency measures of the MEG field gradient waveforms were almost as replicable as those of the dipole models. The results suggest that both MEG and EEG can be used for investigating the simultaneous activity of the left and right auditory cortices independently, MEG being superior in certain experimental setups.     

Cognitive neuroscience of creativity: EEG based approaches

Cognitive neuroscience of creativity has been extensively studied using non-invasive electrical recordings from the scalp called electroencephalograms (EEGs) and event related potentials (ERPs). The paper discusses major aspects of performing research using EEG/ERP based experiments including the recording of the signals, removing noise, estimating ERP signals, and signal analysis for better understanding of the neural correlates of processes involved in creativity. Important factors to be kept in mind to record clean EEG signal in creativity research are discussed. The recorded EEG signal can be corrupted by various sources of noise and methodologies to handle the presence of unwanted artifacts and filtering noise are presented followed by methods to estimate ERPs from the EEG signals from multiple trials. The EEG and ERP signals are further analyzed using various techniques including spectral analysis, coherence analysis, and non-linear signal analysis. These analysis techniques provide a way to understand the spatial activations and temporal development of large scale electrical activity in the brain during creative tasks. The use of this methodology will further enhance our understanding the processes neural and cognitive processes involved in creativity.     

EEG classification of driver mental states by deep learning

Driver fatigue is attracting more and more attention, as it is the main cause of traffic accidents, which bring great harm to society and families. This paper proposes to use deep convolutional neural networks, and deep residual learning, to predict the mental states of drivers from electroencephalography (EEG) signals. Accordingly we have developed two mental state classification models called EEG-Conv and EEG-Conv-R. Tested on intra- and inter-subject, our results show that both models outperform the traditional LSTM- and SVM-based classifiers. Our major findings include (1) Both EEG-Conv and EEG-Conv-R yield very good classification performance for mental state prediction; (2) EEG-Conv-R is more suitable for inter-subject mental state prediction; (3) EEG-Conv-R converges more quickly than EEG-Conv. In summary, our proposed classifiers have better predictive power and are promising for application in practical brain-computer interaction .     

Functional Brain Connectivity as a New Feature for P300 Speller

The brain is a large-scale complex network often referred to as the “connectome”. Cognitive functions and information processing are mainly based on the interactions between distant brain regions. However, most of the ‘feature extraction’ methods used in the context of Brain Computer Interface (BCI) ignored the possible functional relationships between different signals recorded from distinct brain areas. In this paper, the functional connectivity quantified by the phase locking value (PLV) was introduced to characterize the evoked responses (ERPs) obtained in the case of target and non-targets visual stimuli. We also tested the possibility of using the functional connectivity in the context of ‘P300 speller’. The proposed approach was compared to the well-known methods proposed in the state of the art of “P300 Speller”, mainly the peak picking, the area, time/frequency based features, the xDAWN spatial filtering and the stepwise linear discriminant analysis (SWLDA). The electroencephalographic (EEG) signals recorded from ten subjects were analyzed offline. The results indicated that phase synchrony offers relevant information for the classification in a P300 speller. High synchronization between the brain regions was clearly observed during target trials, although no significant synchronization was detected for a non-target trial. The results showed also that phase synchrony provides higher performance than some existing methods for letter classification in a P300 speller principally when large number of trials is available. Finally, we tested the possible combination of both approaches (classical features and phase synchrony). Our findings showed an overall improvement of the performance of the P300-speller when using Peak picking, the area and frequency based features. Similar performances were obtained compared to xDAWN and SWLDA when using large number of trials.     

Auditory Conflict Resolution Correlates with Medial–Lateral Frontal Theta/Alpha Phase Synchrony

When multiple persons speak simultaneously, it may be difficult for the listener to direct attention to correct sound objects among conflicting ones. This could occur, for example, in an emergency situation in which one hears conflicting instructions and the loudest, instead of the wisest, voice prevails. Here, we used cortically-constrained oscillatory MEG/EEG estimates to examine how different brain regions, including caudal anterior cingulate (cACC) and dorsolateral prefrontal cortices (DLPFC), work together to resolve these kinds of auditory conflicts. During an auditory flanker interference task, subjects were presented with sound patterns consisting of three different voices, from three different directions (45° left, straight ahead, 45° right), sounding out either the letters “A” or “O”. They were asked to discriminate which sound was presented centrally and ignore the flanking distracters that were phonetically either congruent (50%) or incongruent (50%) with the target. Our cortical MEG/EEG oscillatory estimates demonstrated a direct relationship between performance and brain activity, showing that efficient conflict resolution, as measured with reduced conflict-induced RT lags, is predicted by theta/alpha phase coupling between cACC and right lateral frontal cortex regions intersecting the right frontal eye fields (FEF) and DLPFC, as well as by increased pre-stimulus gamma (60–110 Hz) power in the left inferior fontal cortex. Notably, cACC connectivity patterns that correlated with behavioral conflict-resolution measures were found during both the pre-stimulus and the pre-response periods. Our data provide evidence that, instead of being only transiently activated upon conflict detection, cACC is involved in sustained engagement of attentional resources required for effective sound object selection performance.     

Eigenspace Time Frequency Based Features for Accurate Seizure Detection from EEG Data

Background

Epilepsy is a neurological disorder that affects over 2% of the world population. Epilepsy patients suffer from recurring seizures that can be very harmful. The unpredictability of seizures is a major concern for medical practitioners because uncontrollable seizures can lead to sudden death and morbidity. A system that could warn patients and doctors alike about the impending seizure event would dramatically enhance the quality of life for patients.