Automatic classification of artifactual ICA-components for artifact removal in EEG signals

Non-invasive decoding of hand movements from electroencephalography based on a hierarchical linear regression model

A non-invasive brain-computer interface (BCI) is an assistive technology with basic communication and control capabilities that decodes continuous electroencephalography (EEG) signals generated by the human brain and converts them into commands to control external devices naturally. However, the decoding efficiency is limited at present because it is unclear which decoding parameters can be used to effectively improve the overall decoding performance. In this paper, five subjects performed experiments involving self-initiated upper-limb movements during three experimental phases. The decoding method based on a hierarchical linear regression (HLR) model was devised to investigate the influence of decoding efficiency according to the characteristic parameters of brain functional networks. Then the optimal set of channels and most sensitive frequency bands were selected using the p value from a Kruskal-Wallis test in the experimental phases. Eventually, the trajectories of free movement and conical helix movement could be decoded using HLR. The experimental result showed that the Pearson correlation coefficient (R) between the measured and decoded paths is 0.66 with HLR, which was higher than the value of 0.46 obtained with the multiple linear regression model. The HLR from a decoding efficiency perspective holds promise for the development of EEG-based BCI to aid in the restoration of hand movements in post-stroke rehabilitation.

Correlation of neural activity with behavioral kinematics reveals distinct sensory encoding and evidence accumulation processes during active tactile sensing

Many real-world decisions rely on active sensing, a dynamic process for directing our sensors (e.g. eyes or fingers) across a stimulus to maximize information gain. Though ecologically pervasive, limited work has focused on identifying neural correlates of the active sensing process. In tactile perception, we often make decisions about an object/surface by actively exploring its shape/texture. Here we investigate the neural correlates of active tactile decision-making by simultaneously measuring electroencephalography (EEG) and finger kinematics while subjects interrogated a haptic surface to make perceptual judgments. Since sensorimotor behavior underlies decision formation in active sensing tasks, we hypothesized that the neural correlates of decision-related processes would be detectable by relating active sensing to neural activity. Novel brain-behavior correlation analysis revealed that three distinct EEG components, localizing to right-lateralized occipital cortex (LOC), middle frontal gyrus (MFG), and supplementary motor area (SMA), respectively, were coupled with active sensing as their activity significantly correlated with finger kinematics. To probe the functional role of these components, we fit their single-trial-couplings to decision-making performance using a hierarchical-drift-diffusion-model (HDDM), revealing that the LOC modulated the encoding of the tactile stimulus whereas the MFG predicted the rate of information integration towards a choice. Interestingly, the MFG disappeared from components uncovered from control subjects performing active sensing but not required to make perceptual decisions. By uncovering the neural correlates of distinct stimulus encoding and evidence accumulation processes, this study delineated, for the first time, the functional role of cortical areas in active tactile decision-making.

Identifying the relationship between oscillatory dynamics and event-related responses

Event related potentials (ERPs) and time frequency analysis of the EEG can identify the temporally distinct coordination of groups of neurons across brain regions during sentence processing. Although there are strong arguments that ERP components and neural oscillations are driven by the same changes in the neural signal, others argue that the lack of clear associations between the two suggests oscillatory dynamics are more than just time frequency representations of ERP components, making it unclear how the two are related. The current study seeks to examine the neural activity underlying auditory sentence processing of both semantic and syntactic errors to clarify if ERP and time frequency analyses identify the same or unique neural responses. Thirty-nine adults completed an auditory semantic judgment task and a grammaticality judgment task. As expected, the semantic judgment task elicited a larger N400 and greater increase in theta power for semantic errors compared to correct sentences and the syntactic judgment task elicited a greater P600 and beta power decrease for both grammatical error types compared to syntactically correct sentences. Importantly, we identified a significant relationship between the N400 and P600 ERPs and theta and beta oscillatory dynamics during semantic and syntactic processing. These findings suggest that ERPs and neural oscillations measure similar neural processes; however, unaccounted for variance may indicate that neural oscillations provide additional information regarding fluctuations in power within a given frequency band. Future studies that vary semantic and syntactic complexity are necessary to understand the cognitive processes that are indexed by these oscillations.

Automated detection of electroencephalography artifacts in human, rodent and canine subjects using machine learning

BACKGROUND

Electroencephalography (EEG) invariably contains extra-cranial artifacts that are commonly dealt with based on qualitative and subjective criteria. Failure to account for EEG artifacts compromises data interpretation.

NEW METHOD

We have developed a quantitative and automated support vector machine (SVM)-based algorithm to accurately classify artifactual EEG epochs in awake rodent, canine and humans subjects. An embodiment of this method also enables the determination of 'eyes open/closed' states in human subjects.

RESULTS

The levels of SVM accuracy for artifact classification in humans, Sprague Dawley rats and beagle dogs were 94.17%, 83.68%, and 85.37%, respectively, whereas 'eyes open/closed' states in humans were labeled with 88.60% accuracy. Each of these results was significantly higher than chance.

COMPARISON WITH EXISTING METHODS

Other existing methods, like those dependent on Independent Component Analysis, have not been tested in non-human subjects, and require full EEG montages, instead of only single channels, as this method does.

CONCLUSIONS

We conclude that our EEG artifact detection algorithm provides a valid and practical solution to a common problem in the quantitative analysis and assessment of EEG in pre-clinical research settings across evolutionary spectra.

Neural indicators of the depth of cognitive processing for user-adaptive neurotechnological applications

The ability to infer implicit user variables in realtime and in an unobtrusive way would open a broad variety of applications such as adapting the user interface in human-computer interaction or developing safety assistance systems in industrial workplaces. Such information may be extracted from behavior, peripheral physiology and brain activity. Each of these sensors has its advantages and disadvantages suggesting that finally all available features should be fused. While in Brain-Computer Interface (BCI) research powerful methods for the real-time extraction of information from brain signals have been developed, comparatively little effort was spent on the extraction of hidden user states. As a further step in this direction, we propose a novel experimental paradigm to study the feasibility of quantifying how deeply presented information is processed in the brain. An investigation of event-related potentials (ERPs) demonstrates the effectiveness of our task in inducing different levels of cognitive processing and shows which features of brain activity provide discriminative information.

Emotional arousal deficit or emotional regulation bias? An electrophysiological study of age-related differences in emotion perception

Background/Study context: Adult age differences in emotion processing have been attributed to age-related decline in earlier emotional perception and age-related bias in later emotional regulation. Yet, the relationship between the processes of early emotion perception and bias in emotional regulation and their influence on behavioral outcomes remains unclear. Event-related potentials (ERPs) have the temporal precision to allow for the online measure of neurophysiological activity and provide potential insight into the complex dynamics of emotion processing and aging.

METHODS

ERPs were used as the primary measure to examine the hypotheses that younger adults will differ in emotional arousal and emotional bias as represented by the early P1 waveform and later P3 waveform, respectively. Thirty-two younger and older adults (16 each) performed a facial emotion discrimination task in which they identified standardized angry, happy, or neutral expressions of faces from the NimStim database.

RESULTS

Younger adults showed a greater P1 ERP for angry faces relative to happy faces at parietal channels, while older adults did not exhibit any emotional modulation of the P1. In contrast, both younger and older adults showed a greater late P3 ERP for angry faces compared to happy faces.

CONCLUSION

The authors' results provide evidence for an age-related deficit in early emotion perception and autonomic arousal. Younger adults, but not older adults, exhibited a pattern of neurophysiological activity believed to reflect preconscious and reflexive identification of threat. Despite these age group differences in early emotion processing, younger and older adults did not exhibit differences in neurophysiological processes believed to reflect emotion regulation.

Automatic Removal of Physiological Artifacts in EEG: The Optimized Fingerprint Method for Sports Science Applications

Data contamination due to physiological artifacts such as those generated by eyeblinks, eye movements, and muscle activity continues to be a central concern in the acquisition and analysis of electroencephalographic (EEG) data. This issue is further compounded in EEG sports science applications where the presence of artifacts is notoriously difficult to control because behaviors that generate these interferences are often the behaviors under investigation. Therefore, there is a need to develop effective and efficient methods to identify physiological artifacts in EEG recordings during sports applications so that they can be isolated from cerebral activity related to the activities of interest. We have developed an EEG artifact detection model, the Fingerprint Method, which identifies different spatial, temporal, spectral, and statistical features indicative of physiological artifacts and uses these features to automatically classify artifactual independent components in EEG based on a machine leaning approach. Here, we optimized our method using artifact-rich training data and a procedure to determine which features were best suited to identify eyeblinks, eye movements, and muscle artifacts. We then applied our model to an experimental dataset collected during endurance cycling. Results reveal that unique sets of features are suitable for the detection of distinct types of artifacts and that the Optimized Fingerprint Method was able to correctly identify over 90% of the artifactual components with physiological origin present in the experimental data. These results represent a significant advancement in the search for effective means to address artifact contamination in EEG sports science applications.

Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition

Independent Component Analysis (ICA) has proven to be an effective data driven method for analyzing EEG data, separating signals from temporally and functionally independent brain and non-brain source processes and thereby increasing their definition. Dimension reduction by Principal Component Analysis (PCA) has often been recommended before ICA decomposition of EEG data, both to minimize the amount of required data and computation time. Here we compared ICA decompositions of fourteen 72-channel single subject EEG data sets obtained (i) after applying preliminary dimension reduction by PCA, (ii) after applying no such dimension reduction, or else (iii) applying PCA only. Reducing the data rank by PCA (even to remove only 1% of data variance) adversely affected both the numbers of dipolar independent components (ICs) and their stability under repeated decomposition. For example, decomposing a principal subspace retaining 95% of original data variance reduced the mean number of recovered 'dipolar' ICs from 30 to 10 per data set and reduced median IC stability from 90% to 76%. PCA rank reduction also decreased the numbers of near-equivalent ICs across subjects. For instance, decomposing a principal subspace retaining 95% of data variance reduced the number of subjects represented in an IC cluster accounting for frontal midline theta activity from 11 to 5. PCA rank reduction also increased uncertainty in the equivalent dipole positions and spectra of the IC brain effective sources. These results suggest that when applying ICA decomposition to EEG data, PCA rank reduction should best be avoided.

The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data

Electroenchephalography (EEG) recordings collected with developmental populations present particular challenges from a data processing perspective. These EEGs have a high degree of artifact contamination and often short recording lengths. As both sample sizes and EEG channel densities increase, traditional processing approaches like manual data rejection are becoming unsustainable. Moreover, such subjective approaches preclude standardized metrics of data quality, despite the heightened importance of such measures for EEGs with high rates of initial artifact contamination. There is presently a paucity of automated resources for processing these EEG data and no consistent reporting of data quality measures. To address these challenges, we propose the Harvard Automated Processing Pipeline for EEG (HAPPE) as a standardized, automated pipeline compatible with EEG recordings of variable lengths and artifact contamination levels, including high-artifact and short EEG recordings from young children or those with neurodevelopmental disorders. HAPPE processes event-related and resting-state EEG data from raw files through a series of filtering, artifact rejection, and re-referencing steps to processed EEG suitable for time-frequency-domain analyses. HAPPE also includes a post-processing report of data quality metrics to facilitate the evaluation and reporting of data quality in a standardized manner. Here, we describe each processing step in HAPPE, perform an example analysis with EEG files we have made freely available, and show that HAPPE outperforms seven alternative, widely-used processing approaches. HAPPE removes more artifact than all alternative approaches while simultaneously preserving greater or equivalent amounts of EEG signal in almost all instances. We also provide distributions of HAPPE's data quality metrics in an 867 file dataset as a reference distribution and in support of HAPPE's performance across EEG data with variable artifact contamination and recording lengths. HAPPE software is freely available under the terms of the GNU General Public License at https://github.com/lcnhappe/happe.

A new ICA-based fingerprint method for the automatic removal of physiological artifacts from EEG recordings

Background

EEG may be affected by artefacts hindering the analysis of brain signals. Data-driven methods like independent component analysis (ICA) are successful approaches to remove artefacts from the EEG. However, the ICA-based methods developed so far are often affected by limitations, such as: the need for visual inspection of the separated independent components (subjectivity problem) and, in some cases, for the independent and simultaneous recording of the inspected artefacts to identify the artefactual independent components; a potentially heavy manipulation of the EEG signals; the use of linear classification methods; the use of simulated artefacts to validate the methods; no testing in dry electrode or high-density EEG datasets; applications limited to specific conditions and electrode layouts.

Methods

Our fingerprint method automatically identifies EEG ICs containing eyeblinks, eye movements, myogenic artefacts and cardiac interference by evaluating 14 temporal, spatial, spectral, and statistical features composing the IC fingerprint. Sixty-two real EEG datasets containing cued artefacts are recorded with wet and dry electrodes (128 wet and 97 dry channels). For each artefact, 10 nonlinear SVM classifiers are trained on fingerprints of expert-classified ICs. Training groups include randomly chosen wet and dry datasets decomposed in 80 ICs. The classifiers are tested on the IC-fingerprints of different datasets decomposed into 20, 50, or 80 ICs. The SVM performance is assessed in terms of accuracy, False Omission Rate (FOR), Hit Rate (HR), False Alarm Rate (FAR), and sensitivity (p). For each artefact, the quality of the artefact-free EEG reconstructed using the classification of the best SVM is assessed by visual inspection and SNR.

Results

The best SVM classifier for each artefact type achieved average accuracy of 1 (eyeblink), 0.98 (cardiac interference), and 0.97 (eye movement and myogenic artefact). Average classification sensitivity (p) was 1 (eyeblink), 0.997 (myogenic artefact), 0.98 (eye movement), and 0.48 (cardiac interference). Average artefact reduction ranged from a maximum of 82% for eyeblinks to a minimum of 33% for cardiac interference, depending on the effectiveness of the proposed method and the amplitude of the removed artefact. The performance of the SVM classifiers did not depend on the electrode type, whereas it was better for lower decomposition levels (50 and 20 ICs).

Discussion

Apart from cardiac interference, SVM performance and average artefact reduction indicate that the fingerprint method has an excellent overall performance in the automatic detection of eyeblinks, eye movements and myogenic artefacts, which is comparable to that of existing methods. Being also independent from simultaneous artefact recording, electrode number, type and layout, and decomposition level, the proposed fingerprint method can have useful applications in clinical and experimental EEG settings.

Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering

Electroencephalogram (EEG) signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA), feature extraction, and the Gaussian mixture model (GMM) to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research.

ARTIST: A fully automated artifact rejection algorithm for single-pulse TMS-EEG data

Concurrent single-pulse TMS-EEG (spTMS-EEG) is an emerging noninvasive tool for probing causal brain dynamics in humans. However, in addition to the common artifacts in standard EEG data, spTMS-EEG data suffer from enormous stimulation-induced artifacts, posing significant challenges to the extraction of neural information. Typically, neural signals are analyzed after a manual time-intensive and often subjective process of artifact rejection. Here we describe a fully automated algorithm for spTMS-EEG artifact rejection. A key step of this algorithm is to decompose the spTMS-EEG data into statistically independent components (ICs), and then train a pattern classifier to automatically identify artifact components based on knowledge of the spatio-temporal profile of both neural and artefactual activities. The autocleaned and hand-cleaned data yield qualitatively similar group evoked potential waveforms. The algorithm achieves a 95% IC classification accuracy referenced to expert artifact rejection performance, and does so across a large number of spTMS-EEG data sets (n = 90 stimulation sites), retains high accuracy across stimulation sites/subjects/populations/montages, and outperforms current automated algorithms. Moreover, the algorithm was superior to the artifact rejection performance of relatively novice individuals, who would be the likely users of spTMS-EEG as the technique becomes more broadly disseminated. In summary, our algorithm provides an automated, fast, objective, and accurate method for cleaning spTMS-EEG data, which can increase the utility of TMS-EEG in both clinical and basic neuroscience settings.

Removal of muscular artifacts in EEG signals: a comparison of linear decomposition methods

The most common approach to reduce muscle artifacts in electroencephalographic signals is to linearly decompose the signals in order to separate artifactual from neural sources, using one of several variants of independent component analysis (ICA). Here we compare three of the most commonly used ICA methods (extended Infomax, FastICA and TDSEP) with two other linear decomposition methods (Fourier-ICA and spatio-spectral decomposition) suitable for the extraction of oscillatory activity. We evaluate the methods’ ability to remove event-locked muscle artifacts while maintaining event-related desynchronization in data from 18 subjects who performed self-paced foot movements. We find that all five analyzed methods drastically reduce the muscle artifacts. For the three ICA methods, adequately high-pass filtering is very important. Compared to the effect of high-pass filtering, differences between the five analyzed methods were small, with extended Infomax performing best.

An 18-subject EEG data collection using a visual-oddball task, designed for benchmarking algorithms and headset performance comparisons

This data note describes an 18-subject EEG (electroencephalogram) data collection from an experiment in which subjects performed a standard visual oddball task. Several research projects have used this data to test artifact detection, classification, transfer learning, EEG preprocessing, blink detection, and automated annotation algorithms. We are releasing the data in three formats to enable benchmarking of EEG algorithms in many areas. The data was acquired using a Biosemi Active 2 EEG headset and includes 64 channels of EEG, 4 channels of EOG (electrooculogram), and 2 mastoid reference channels.

Electrophysiology Reveals the Neural Dynamics of Naturalistic Auditory Language Processing: Event-Related Potentials Reflect Continuous Model Updates

The recent trend away from ANOVA-based analyses places experimental investigations into the neurobiology of cognition in more naturalistic and ecologically valid designs within reach. Using mixed-effects models for epoch-based regression, we demonstrate the feasibility of examining event-related potentials (ERPs), and in particular the N400, to study the neural dynamics of human auditory language processing in a naturalistic setting. Despite the large variability between trials during naturalistic stimulation, we replicated previous findings from the literature: the effects of frequency, animacy, and word order and find previously unexplored interaction effects. This suggests a new perspective on ERPs, namely, as a continuous modulation reflecting continuous stimulation instead of a series of discrete and essentially sequential processes locked to discrete events.

Lateralized occipito-temporal N1 responses to images of salient distorted finger postures

For humans as social beings, other people’s hands are highly visually conspicuous. Exceptionally striking are hands in other than natural configuration which have been found to elicit distinct brain activation. Here we studied response strength and lateralization of this activation using event-related potentials (ERPs), in particular, occipito-temporal N1 responses as correlates of activation in extrastriate body area. Participants viewed computer-generated images of hands, half of them showing distorted fingers, the other half showing natural fingers. As control stimuli of similar geometric complexity, images of chairs were shown, half of them with distorted legs, half with standard legs. The contrast of interest was between distorted and natural/standard stimuli. For hands, stronger N1 responses were observed for distorted (vs natural) stimuli from 170 ms post stimulus. Such stronger N1 responses were found for distorted hands and absent for distorted chairs, therefore likely unrelated to visuospatial processing of the unusual distorted shapes. Rather, N1 modulation over both hemispheres – but robustly right-lateralized – could reflect distorted hands as emotionally laden stimuli. The results are in line with privileged visual processing of hands as highly salient body parts, with distortions engaging neural resources that are especially activated for biological stimuli in social perception.

EEG-Annotate: Automated identification and labeling of events in continuous signals with applications to EEG

BACKGROUND

In controlled laboratory EEG experiments, researchers carefully mark events and analyze subject responses time-locked to these events. Unfortunately, such markers may not be available or may come with poor timing resolution for experiments conducted in less-controlled naturalistic environments.

NEW METHOD

We present an integrated event-identification method for identifying particular responses that occur in unlabeled continuously recorded EEG signals based on information from recordings of other subjects potentially performing related tasks. We introduce the idea of timing slack and timing-tolerant performance measures to deal with jitter inherent in such non-time-locked systems. We have developed an implementation available as an open-source MATLAB toolbox (http://github.com/VisLab/EEG-Annotate) and have made test data available in a separate data note.

RESULTS

We applied the method to identify visual presentation events (both target and non-target) in data from an unlabeled subject using labeled data from other subjects with good sensitivity and specificity. The method also identified actual visual presentation events in the data that were not previously marked in the experiment.

COMPARISON WITH EXISTING METHODS

Although the method uses traditional classifiers for initial stages, the problem of identifying events based on the presence of stereotypical EEG responses is the converse of the traditional stimulus-response paradigm and has not been addressed in its current form.

CONCLUSIONS

In addition to identifying potential events in unlabeled or incompletely labeled EEG, these methods also allow researchers to investigate whether particular stereotypical neural responses are present in other circumstances. Timing-tolerance has the added benefit of accommodating inter- and intra- subject timing variations.

Assessing the Depth of Cognitive Processing as the Basis for Potential User-State Adaptation

Objective: Decoding neurocognitive processes on a single-trial basis with Brain-Computer Interface (BCI) techniques can reveal the user's internal interpretation of the current situation. Such information can potentially be exploited to make devices and interfaces more user aware. In this line of research, we took a further step by studying neural correlates of different levels of cognitive processes and developing a method that allows to quantify how deeply presented information is processed in the brain. Methods/Approach: Seventeen participants took part in an EEG study in which we evaluated different levels of cognitive processing (no processing, shallow, and deep processing) within three distinct domains (memory, language, and visual imagination). Our investigations showed gradual differences in the amplitudes of event-related potentials (ERPs) and in the extend and duration of event-related desynchronization (ERD) which both correlate with task difficulty. We performed multi-modal classification to map the measured correlates of neurocognitive processing to the corresponding level of processing. Results: Successful classification of the neural components was achieved, which reflects the level of cognitive processing performed by the participants. The results show performances above chance level for each participant and a mean performance of 70-90% for all conditions and classification pairs. Significance: The successful estimation of the level of cognition on a single-trial basis supports the feasibility of user-state adaptation based on ongoing neural activity. There is a variety of potential use cases such as: a user-friendly adaptive design of an interface or the development of assistance systems in safety critical workplaces.

Macronutrient composition of a morning meal and the maintenance of attention throughout the morning

BACKGROUND

At present, the impact of macronutrient composition and nutrient intake on sustained attention in adults is unclear, although some prior work suggests that nutritive interventions that engender slow, steady glucose availability support sustained attention after consumption. A separate line of evidence suggests that nutrient consumption may alter electroencephalographic markers of neurophysiological activity, including neural oscillations in the alpha-band (8-14 Hz), which are known to be richly interconnected with the allocation of attention. It is here investigated whether morning ingestion of foodstuffs with differing macronutrient compositions might differentially impact the allocation of sustained attention throughout the day as indexed by both behavior and the deployment of attention-related alpha-band activity.

METHODS

Twenty-four adult participants were recruited into a three-day study with a cross-over design that employed a previously validated sustained attention task (the Spatial CTET). On each experimental day, subjects consumed one of three breakfasts with differing carbohydrate availabilities (oatmeal, cornflakes, and water) and completed blocks of the Spatial CTET throughout the morning while behavioral performance, subjective metrics of hunger/fullness, and electroencephalographic (EEG) measurements of alpha oscillatory activity were recorded.

RESULTS

Although behavior and electrophysiological metrics changed over the course of the day, no differences in their trajectories were observed as a function of breakfast condition. However, subjective metrics of hunger/fullness revealed that caloric interventions (oatmeal and cornflakes) reduced hunger across the experimental day with respect to the non-caloric, volume-matched control (water). Yet, no differences in hunger/fullness were observed between the oatmeal and cornflakes interventions.

CONCLUSION

Observation of a relationship between macronutrient intervention and sustained attention (if one exists) will require further standardization of empirical investigations to aid in the synthesis and replicability of results. In addition, continued implementation of neurophysiological markers in this domain is encouraged, as they often produce nuanced insight into cognition even in the absence of overt behavioral changes. ClinicalTrials.gov Identifier: NCT03169283.

Analysis of functional brain connections for positive-negative emotions using phase locking value

In this study, we investigate the brain networks during positive and negative emotions for different types of stimulus (audio only, video only and audio + video) in [Formula: see text], and [Formula: see text] bands in terms of phase locking value, a nonlinear method to study functional connectivity. Results show notable hemispheric lateralization as phase synchronization values between channels are significant and high in right hemisphere for all emotions. Left frontal electrodes are also found to have control over emotion in terms of functional connectivity. Besides significant inter-hemisphere phase locking values are observed between left and right frontal regions, specifically between left anterior frontal and right mid-frontal, inferior-frontal and anterior frontal regions; and also between left and right mid frontal regions. ANOVA analysis for stimulus types show that stimulus types are not separable for emotions having high valence. PLV values are significantly different only for negative emotions or neutral emotions between audio only/video only and audio only/audio + video stimuli. Finding no significant difference between video only and audio + video stimuli is interesting and might be interpreted as that video content is the most effective part of a stimulus.

Connectome pattern alterations with increment of mental fatigue in one-hour driving simulation

The importance of understanding mental fatigue can be seen from many studies that started back in past decades. It is only until recent years has mental fatigue been explored through connectivity network analysis using graph theory. Although previous studies have revealed certain properties of the mental fatigue network via graph theory, some of these findings seemingly conflict with one another. The differences in findings could be due to mental fatigue being caused by various factors or being analyzed using different methods. So, in this study, to further understand the functional connectivity of driving fatigue, a weighted and undirected connectivity matrix would be constructed before applying graph theory to identify the biomarker from the network property. To obtain data for analysis, a 64-channel EEG cap was used to record the brain signals of subjects undergoing a one-hour driving simulation. Using the recorded EEG signal, a connectivity matrix was constructed using a synchronous method known as phase lag index (PLI) for the graph theory analysis. Results from this graph theory analysis showed that the synchronous network had increased clustering coefficient and decreased path length with the accumulation of mental fatigue. Furthermore, by calculating clustering coefficient regionally, its results revealed that the significant increase occurred mainly in the parietal and occipital regions of the brain.

Domain-general neural correlates of dependency formation: Using complex tones to simulate language

There is an ongoing debate whether the P600 event-related potential component following syntactic anomalies reflects syntactic processes per se, or if it is an instance of the P300, a domain-general ERP component associated with attention and cognitive reorientation. A direct comparison of both components is challenging because of the huge discrepancy in experimental designs and stimulus choice between language and 'classic' P300 experiments. In the present study, we develop a new approach to mimic the interplay of sequential position as well as categorical and relational information in natural language syntax (word category and agreement) in a non-linguistic target detection paradigm using musical instruments. Participants were instructed to (covertly) detect target tones which were defined by instrument change and pitch rise between subsequent tones at the last two positions of four-tone sequences. We analysed the EEG using event-related averaging and time-frequency decomposition. Our results show striking similarities to results obtained from linguistic experiments. We found a P300 that showed sensitivity to sequential position and a late positivity sensitive to stimulus type and position. A time-frequency decomposition revealed significant effects of sequential position on the theta band and a significant influence of stimulus type on the delta band. Our results suggest that the detection of non-linguistic targets defined via complex feature conjunctions in the present study and the detection of syntactic anomalies share the same underlying processes: attentional shift and memory based matching processes that act upon multi-feature conjunctions. We discuss the results as supporting domain-general accounts of the P600 during natural language comprehension.

BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis

Electroencephalography (EEG) offers a platform for studying the relationships between behavioral measures, such as blink rate and duration, with neural correlates of fatigue and attention, such as theta and alpha band power. Further, the existence of EEG studies covering a variety of subjects and tasks provides opportunities for the community to better characterize variability of these measures across tasks and subjects. We have implemented an automated pipeline (BLINKER) for extracting ocular indices such as blink rate, blink duration, and blink velocity-amplitude ratios from EEG channels, EOG channels, and/or independent components (ICs). To illustrate the use of our approach, we have applied the pipeline to a large corpus of EEG data (comprising more than 2000 datasets acquired at eight different laboratories) in order to characterize variability of certain ocular indicators across subjects. We also investigate dependence of ocular indices on task in a shooter study. We have implemented our algorithms in a freely available MATLAB toolbox called BLINKER. The toolbox, which is easy to use and can be applied to collections of data without user intervention, can automatically discover which channels or ICs capture blinks. The tools extract blinks, calculate common ocular indices, generate a report for each dataset, dump labeled images of the individual blinks, and provide summary statistics across collections. Users can run BLINKER as a script or as a plugin for EEGLAB. The toolbox is available at https://github.com/VisLab/EEG-Blinks. User documentation and examples appear at http://vislab.github.io/EEG-Blinks/.

Effects of bupropion sustained release on task-related EEG alpha activity in smokers: Individual differences in drug response

The mechanisms underlying bupropion's efficacy as an antidepressant and a smoking cessation aid are far from being fully characterized. The present study is the first to examine the effects of bupropion on visuospatial task-related parietal EEG alpha power asymmetry-an asymmetry that has previously been found to be associated with severity of depressive symptoms (i.e., the more depressive symptoms, the greater alpha power in the right vs. left parietal area [Henriques & Davidson, 1997; Rabe, Debener, Brocke, & Beauducel, 2005]). Participants, all of whom were smokers and none of whom were clinically depressed, were randomly assigned to the Placebo group (n = 79) or Bupropion group (n = 31) in a double-blind study. EEG during the performance of the visuospatial task was collected before and after 14 days on placebo or bupropion sustained-release capsules. Relative to the Placebo group, the Bupropion group (especially, the Bupropion subgroup who had a positive right versus left parietal alpha power asymmetry at pretreatment) had a reduction in the parietal alpha asymmetry (driven largely by a decrease in right parietal alpha power). These findings support the hypothesis that bupropion can induce changes in parietal EEG asymmetry that have been shown in previous literature to be associated with a reduction in depressive states and traits. (PsycINFO Database Record

A new method for quantifying the performance of EEG blind source separation algorithms by referencing a simultaneously recorded ECoG signal

Blind source separation (BSS) algorithms extract neural signals from electroencephalography (EEG) data. However, it is difficult to quantify source separation performance because there is no criterion to dissociate neural signals and noise in EEG signals. This study develops a method for evaluating BSS performance. The idea is neural signals in EEG can be estimated by comparison with simultaneously measured electrocorticography (ECoG). Because the ECoG electrodes cover the majority of the lateral cortical surface and should capture most of the original neural sources in the EEG signals. We measured real EEG and ECoG data and developed an algorithm for evaluating BSS performance. First, EEG signals are separated into EEG components using the BSS algorithm. Second, the EEG components are ranked using the correlation coefficients of the ECoG regression and the components are grouped into subsets based on their ranks. Third, canonical correlation analysis estimates how much information is shared between the subsets of the EEG components and the ECoG signals. We used our algorithm to compare the performance of BSS algorithms (PCA, AMUSE, SOBI, JADE, fastICA) via the EEG and ECoG data of anesthetized nonhuman primates. The results (Best case >JADE = fastICA >AMUSE = SOBI ≥ PCA >random separation) were common to the two subjects. To encourage the further development of better BSS algorithms, our EEG and ECoG data are available on our Web site (http://neurotycho.org/) as a common testing platform.

Localized component filtering for electroencephalogram artifact rejection

Blind source separation (BSS) based artifact rejection systems have been extensively studied in the electroencephalogram (EEG) literature. Although there have been advances in the development of techniques capable of dissociating neural and artifactual activity, these are still not perfect. As a result, a compromise between reduction of noise and leakage of neural activity has to be found. Here, we propose a new methodology to enhance the performance of existing BSS systems: Localized component filtering (LCF). In essence, LCF identifies the artifactual time segments within each component extracted by BSS and restricts the processing of components to these segments, therefore reducing neural leakage. We show that LCF can substantially reduce the neural leakage, increasing the true acceptance rate by 22 percentage points while worsening the false acceptance rate by less than 2 percentage points in a dataset consisting of simulated EEG data (4% improvement of the correlation between original and cleaned signals). Evaluated on real EEG data, we observed a significant increase of the signal-to-noise ratio of up to 9%.

Therapy-Induced Neuroplasticity of Language in Chronic Post Stroke Aphasia: A Mismatch Negativity Study of (A)Grammatical and Meaningful/less Mini-Constructions

Clinical language performance and neurophysiological correlates of language processing were measured before and after intensive language therapy in patients with chronic (time post stroke >1 year) post stroke aphasia (PSA). As event-related potential (ERP) measure, the mismatch negativity (MMN) was recorded in a distracted oddball paradigm to short spoken sentences. Critical 'deviant' sentence stimuli where either well-formed and meaningful, or syntactically, or lexico-semantically incorrect. After 4 weeks of speech-language therapy (SLT) delivered with high intensity (10.5 h per week), clinical language assessment with the Aachen Aphasia Test battery demonstrated significant linguistic improvements, which were accompanied by enhanced MMN responses. More specifically, MMN amplitudes to grammatically correct and meaningful mini-constructions and to 'jabberwocky' sentences containing a pseudoword significantly increased after therapy. However, no therapy-related changes in MMN responses to syntactically incorrect strings including agreement violations were observed. While MMN increases to well-formed meaningful strings can be explained both at the word and construction levels, the neuroplastic change seen for 'jabberwocky' sentences suggests an explanation in terms of constructions. The results confirm previous reports that intensive SLT leads to improvements of linguistic skills in chronic aphasia patients and now demonstrate that this clinical improvement is associated with enhanced automatic brain indexes of construction processing, although no comparable change is present for ungrammatical strings. Furthermore, the data confirm that the language-induced MMN is a useful tool to map functional language recovery in PSA.

Automated classification of pain perception using high-density electroencephalography data

The translation of brief, millisecond-long pain-eliciting stimuli to the subjective perception of pain is associated with changes in theta, alpha, beta, and gamma oscillations over sensorimotor cortex. However, when a pain-eliciting stimulus continues for minutes, regions beyond the sensorimotor cortex, such as the prefrontal cortex, are also engaged. Abnormalities in prefrontal cortex have been associated with chronic pain states, but conventional, millisecond-long EEG paradigms do not engage prefrontal regions. In the current study, we collected high-density EEG data during an experimental paradigm in which subjects experienced a 4-s, low- or high-intensity pain-eliciting stimulus. EEG data were analyzed using independent component analyses, EEG source localization analyses, and measure projection analyses. We report three novel findings. First, an increase in pain perception was associated with an increase in gamma and theta power in a cortical region that included medial prefrontal cortex. Second, a decrease in lower beta power was associated with an increase in pain perception in a cortical region that included the contralateral sensorimotor cortex. Third, we used machine learning for automated classification of EEG data into low- and high-pain classes. Theta and gamma power in the medial prefrontal region and lower beta power in the contralateral sensorimotor region served as features for classification. We found a leave-one-out cross-validation accuracy of 89.58%. The development of biological markers for pain states continues to gain traction in the literature, and our findings provide new information that advances this body of work.NEW & NOTEWORTHY The development of a biological marker for pain continues to gain traction in literature. Our findings show that high- and low-pain perception in human subjects can be classified with 89% accuracy using high-density EEG data from prefrontal cortex and contralateral sensorimotor cortex. Our approach represents a novel neurophysiological paradigm that advances the literature on biological markers for pain.

Assessing and conceptualizing frontal EEG asymmetry: An updated primer on recording, processing, analyzing, and interpreting frontal alpha asymmetry

Frontal electroencephalographic (EEG) alpha asymmetry is widely researched in studies of emotion, motivation, and psychopathology, yet it is a metric that has been quantified and analyzed using diverse procedures, and diversity in procedures muddles cross-study interpretation. The aim of this article is to provide an updated tutorial for EEG alpha asymmetry recording, processing, analysis, and interpretation, with an eye towards improving consistency of results across studies. First, a brief background in alpha asymmetry findings is provided. Then, some guidelines for recording, processing, and analyzing alpha asymmetry are presented with an emphasis on the creation of asymmetry scores, referencing choices, and artifact removal. Processing steps are explained in detail, and references to MATLAB-based toolboxes that are helpful for creating and investigating alpha asymmetry are noted. Then, conceptual challenges and interpretative issues are reviewed, including a discussion of alpha asymmetry as a mediator/moderator of emotion and psychopathology. Finally, the effects of two automated component-based artifact correction algorithms-MARA and ADJUST-on frontal alpha asymmetry are evaluated.

Methods for artifact detection and removal from scalp EEG: A review

Electroencephalography (EEG) is the most popular brain activity recording technique used in wide range of applications. One of the commonly faced problems in EEG recordings is the presence of artifacts that come from sources other than brain and contaminate the acquired signals significantly. Therefore, much research over the past 15 years has focused on identifying ways for handling such artifacts in the preprocessing stage. However, this is still an active area of research as no single existing artifact detection/removal method is complete or universal. This article presents an extensive review of the existing state-of-the-art artifact detection and removal methods from scalp EEG for all potential EEG-based applications and analyses the pros and cons of each method. First, a general overview of the different artifact types that are found in scalp EEG and their effect on particular applications are presented. In addition, the methods are compared based on their ability to remove certain types of artifacts and their suitability in relevant applications (only functional comparison is provided not performance evaluation of methods). Finally, the future direction and expected challenges of current research is discussed. Therefore, this review is expected to be helpful for interested researchers who will develop and/or apply artifact handling algorithm/technique in future for their applications as well as for those willing to improve the existing algorithms or propose a new solution in this particular area of research.

Neural mechanisms underlying catastrophic failure in human-machine interaction during aerial navigation

OBJECTIVE

We investigated the neural correlates of workload buildup in a fine visuomotor task called the boundary avoidance task (BAT). The BAT has been known to induce naturally occurring failures of human-machine coupling in high performance aircraft that can potentially lead to a crash-these failures are termed pilot induced oscillations (PIOs).

APPROACH

We recorded EEG and pupillometry data from human subjects engaged in a flight BAT simulated within a virtual 3D environment.

MAIN RESULTS

We find that workload buildup in a BAT can be successfully decoded from oscillatory features in the electroencephalogram (EEG). Information in delta, theta, alpha, beta, and gamma spectral bands of the EEG all contribute to successful decoding, however gamma band activity with a lateralized somatosensory topography has the highest contribution, while theta band activity with a fronto-central topography has the most robust contribution in terms of real-world usability. We show that the output of the spectral decoder can be used to predict PIO susceptibility. We also find that workload buildup in the task induces pupil dilation, the magnitude of which is significantly correlated with the magnitude of the decoded EEG signals. These results suggest that PIOs may result from the dysregulation of cortical networks such as the locus coeruleus (LC)-anterior cingulate cortex (ACC) circuit.

SIGNIFICANCE

Our findings may generalize to similar control failures in other cases of tight man-machine coupling where gains and latencies in the control system must be inferred and compensated for by the human operators. A closed-loop intervention using neurophysiological decoding of workload buildup that targets the LC-ACC circuit may positively impact operator performance in such situations.

On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP

Standard artifact removal methods for electroencephalographic (EEG) signals are either based on Independent Component Analysis (ICA) or they regress out ocular activity measured at electrooculogram (EOG) channels. Successful ICA-based artifact reduction relies on suitable pre-processing. Here we systematically evaluate the effects of high-pass filtering at different frequencies. Offline analyses were based on event-related potential data from 21 participants performing a standard auditory oddball task and an automatic artifactual component classifier method (MARA). As a pre-processing step for ICA, high-pass filtering between 1-2 Hz consistently produced good results in terms of signal-to-noise ratio (SNR), single-trial classification accuracy and the percentage of `near-dipolar' ICA components. Relative to no artifact reduction, ICA-based artifact removal significantly improved SNR and classification accuracy. This was not the case for a regression-based approach to remove EOG artifacts.

EEG-Based BCI System Using Adaptive Features Extraction and Classification Procedures

Motor imagery is a common control strategy in EEG-based brain-computer interfaces (BCIs). However, voluntary control of sensorimotor (SMR) rhythms by imagining a movement can be skilful and unintuitive and usually requires a varying amount of user training. To boost the training process, a whole class of BCI systems have been proposed, providing feedback as early as possible while continuously adapting the underlying classifier model. The present work describes a cue-paced, EEG-based BCI system using motor imagery that falls within the category of the previously mentioned ones. Specifically, our adaptive strategy includes a simple scheme based on a common spatial pattern (CSP) method and support vector machine (SVM) classification. The system's efficacy was proved by online testing on 10 healthy participants. In addition, we suggest some features we implemented to improve a system's “flexibility” and “customizability,” namely, (i) a flexible training session, (ii) an unbalancing in the training conditions, and (iii) the use of adaptive thresholds when giving feedback.

Improving test-retest reliability of quantitative electroencephalography using different preprocessing approaches

This work aims to assess the effect of preprocessing approaches over test-retest reliability of quantitative electroencephalography measurements. Two electroencephalography sessions were recorded during an eyes-closed resting state condition in 15 young healthy individuals. The second session was 4 to 6 weeks after the first one. Clean recordings were obtained from the implementation of different preprocessing approaches commonly used in the literature. We then estimated the power spectrum density, for each individual and preprocessing approach, in six frequency bands: delta, theta, alpha1, alpha2, beta, and gamma. Test-retest reliability using the intraclass correlation coefficient was calculated for power spectrum in each methodology and frequency band. We found that the test-retest reliability varied considerably across frequency bands and preprocessing approaches. Reliability was higher for theta, alpha1, and alpha2 frequency bands. Also, the use of preprocessing approach that includes a robust reference to average and independent component analysis, can improve test-retest reliability in other bands such as beta and gamma. Results suggest that quantitative electroencephalography are test-retest reliable and can be used in longitudinal studies.

Hybrid ICA-Regression: Automatic Identification and Removal of Ocular Artifacts from Electroencephalographic Signals

Electroencephalography (EEG) is a portable brain-imaging technique with the advantage of high-temporal resolution that can be used to record electrical activity of the brain. However, it is difficult to analyze EEG signals due to the contamination of ocular artifacts, and which potentially results in misleading conclusions. Also, it is a proven fact that the contamination of ocular artifacts cause to reduce the classification accuracy of a brain-computer interface (BCI). It is therefore very important to remove/reduce these artifacts before the analysis of EEG signals for applications like BCI. In this paper, a hybrid framework that combines independent component analysis (ICA), regression and high-order statistics has been proposed to identify and eliminate artifactual activities from EEG data. We used simulated, experimental and standard EEG signals to evaluate and analyze the effectiveness of the proposed method. Results demonstrate that the proposed method can effectively remove ocular artifacts as well as it can preserve the neuronal signals present in EEG data. A comparison with four methods from literature namely ICA, regression analysis, wavelet-ICA (wICA), and regression-ICA (REGICA) confirms the significantly enhanced performance and effectiveness of the proposed method for removal of ocular activities from EEG, in terms of lower mean square error and mean absolute error values and higher mutual information between reconstructed and original EEG.

Direction and viewing area-sensitive influence of EOG artifacts revealed in the EEG topographic pattern analysis

The influence of eye movement-related artifacts on electroencephalography (EEG) signals of human subjects, who were requested to perform a direction or viewing area dependent saccade task, was investigated by using a simultaneous recording with ocular potentials as electro-oculography (EOG). In the past, EOG artifact removals have been studied in tasks with a single fixation point in the screen center, with less attention to the sensitivity of cornea-retinal dipole orientations to the EEG head map. In the present study, we hypothesized the existence of a systematic EOG influence that differs according to coupling conditions of eye-movement directions with viewing areas including different fixation points. The effect was validated in the linear regression analysis by using 12 task conditions combining horizontal/vertical eye-movement direction and three segregated zones of gaze in the screen. In the first place, event-related potential topographic patterns were analyzed to compare the 12 conditions and propagation coefficients of the linear regression analysis were successively calculated in each condition. As a result, the EOG influences were significantly different in a large number of EEG channels, especially in the case of horizontal eye-movements. In the cross validation, the linear regression analysis using the appropriate dataset of the target direction/viewing area combination demonstrated an improved performance compared with the traditional methods using a single fixation at the center. This result may open a potential way to improve artifact correction methods by considering the systematic EOG influence that can be predicted according to the view angle such as using eye-tracker systems.

Hybrid EEG--Eye Tracker: Automatic Identification and Removal of Eye Movement and Blink Artifacts from Electroencephalographic Signal

Contamination of eye movement and blink artifacts in Electroencephalogram (EEG) recording makes the analysis of EEG data more difficult and could result in mislead findings. Efficient removal of these artifacts from EEG data is an essential step in improving classification accuracy to develop the brain-computer interface (BCI). In this paper, we proposed an automatic framework based on independent component analysis (ICA) and system identification to identify and remove ocular artifacts from EEG data by using hybrid EEG and eye tracker system. The performance of the proposed algorithm is illustrated using experimental and standard EEG datasets. The proposed algorithm not only removes the ocular artifacts from artifactual zone but also preserves the neuronal activity related EEG signals in non-artifactual zone. The comparison with the two state-of-the-art techniques namely ADJUST based ICA and REGICA reveals the significant improved performance of the proposed algorithm for removing eye movement and blink artifacts from EEG data. Additionally, results demonstrate that the proposed algorithm can achieve lower relative error and higher mutual information values between corrected EEG and artifact-free EEG data.