scholarly journals Subject-Specific Cognitive Workload Classification Using EEG-Based Functional Connectivity and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6710
Author(s):  
Anmol Gupta ◽  
Gourav Siddhad ◽  
Vishal Pandey ◽  
Partha Pratim Roy ◽  
Byung-Gyu Kim
Keyword(s):  
Deep Learning ◽  
Functional Connectivity ◽  
Real Time ◽  
Phase Transfer ◽  
Brain Regions ◽  
Dynamic Decision Making ◽  
High Time Resolution ◽  
Cognitive Workload ◽  
Model Free ◽  
Subject Specific

Cognitive workload is a crucial factor in tasks involving dynamic decision-making and other real-time and high-risk situations. Neuroimaging techniques have long been used for estimating cognitive workload. Given the portability, cost-effectiveness and high time-resolution of EEG as compared to fMRI and other neuroimaging modalities, an efficient method of estimating an individual’s workload using EEG is of paramount importance. Multiple cognitive, psychiatric and behavioral phenotypes have already been known to be linked with “functional connectivity”, i.e., correlations between different brain regions. In this work, we explored the possibility of using different model-free functional connectivity metrics along with deep learning in order to efficiently classify the cognitive workload of the participants. To this end, 64-channel EEG data of 19 participants were collected while they were doing the traditional n-back task. These data (after pre-processing) were used to extract the functional connectivity features, namely Phase Transfer Entropy (PTE), Mutual Information (MI) and Phase Locking Value (PLV). These three were chosen to do a comprehensive comparison of directed and non-directed model-free functional connectivity metrics (allows faster computations). Using these features, three deep learning classifiers, namely CNN, LSTM and Conv-LSTM were used for classifying the cognitive workload as low (1-back), medium (2-back) or high (3-back). With the high inter-subject variability in EEG and cognitive workload and recent research highlighting that EEG-based functional connectivity metrics are subject-specific, subject-specific classifiers were used. Results show the state-of-the-art multi-class classification accuracy with the combination of MI with CNN at 80.87%, followed by the combination of PLV with CNN (at 75.88%) and MI with LSTM (at 71.87%). The highest subject specific performance was achieved by the combinations of PLV with Conv-LSTM, and PLV with CNN with an accuracy of 97.92%, followed by the combination of MI with CNN (at 95.83%) and MI with Conv-LSTM (at 93.75%). The results highlight the efficacy of the combination of EEG-based model-free functional connectivity metrics and deep learning in order to classify cognitive workload. The work can further be extended to explore the possibility of classifying cognitive workload in real-time, dynamic and complex real-world scenarios.

scholarly journals A Deep Learning Model for Data-Driven Discovery of Functional Connectivity

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 75
Author(s):  
Usman Mahmood ◽  
Zening Fu ◽  
Vince D. Calhoun ◽  
Sergey Plis
Keyword(s):  
Deep Learning ◽  
Functional Connectivity ◽  
State Of The Art ◽  
Weighted Graph ◽  
Brain Regions ◽  
Classification Performance ◽  
Class Discrimination ◽  
Post Hoc ◽  
Deep Learning Model ◽  
The Brain

Functional connectivity (FC) studies have demonstrated the overarching value of studying the brain and its disorders through the undirected weighted graph of functional magnetic resonance imaging (fMRI) correlation matrix. However, most of the work with the FC depends on the way the connectivity is computed, and it further depends on the manual post-hoc analysis of the FC matrices. In this work, we propose a deep learning architecture BrainGNN that learns the connectivity structure as part of learning to classify subjects. It simultaneously applies a graphical neural network to this learned graph and learns to select a sparse subset of brain regions important to the prediction task. We demonstrate that the model’s state-of-the-art classification performance on a schizophrenia fMRI dataset and demonstrate how introspection leads to disorder relevant findings. The graphs that are learned by the model exhibit strong class discrimination and the sparse subset of relevant regions are consistent with the schizophrenia literature.

scholarly journals Connectome-wide search for functional connectivity locus associated with pathological rumination as a target for real-time fMRI neurofeedback intervention

2020 ◽  
Author(s):  
Masaya Misaki ◽  
Aki Tsuchiyagaito ◽  
Obada A Zoubi ◽  
Martin Paulus ◽  
Jerzy Bodurka ◽  
...  
Keyword(s):  
Anxiety Disorder ◽  
Functional Connectivity ◽  
Real Time ◽  
Target Location ◽  
Simulation Analysis ◽  
Clinical Symptoms ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Precise Location ◽  
Online Computation

AbstractReal-time fMRI neurofeedback (rtfMRI-nf) enables noninvasive targeted intervention in brain activation with high spatial specificity. To achieve this promise of rtfMRI-nf, we introduced and demonstrated a data-driven framework to design a rtfMRI-nf intervention through the discovery of precise target location associated with clinical symptoms and neurofeedback signal optimization. Specifically, we identified the functional connectivity locus associated with rumination symptoms, utilizing a connectome-wide search in resting-state fMRI data from a large cohort of mood and anxiety disorder individuals (N=223) and healthy controls (N=45). Then, we performed a rtfMRI simulation analysis to optimize the online functional connectivity neurofeedback signal for the identified functional connectivity. The connectome-wide search was performed in the medial prefrontal cortex and the posterior cingulate cortex/precuneus brain regions to identify the precise location of the functional connectivity associated with rumination severity as measured by the ruminative response style (RRS) scale. The analysis found that the functional connectivity between the loci in the precuneus (−6, −54, 48 mm in MNI) and the right temporo-parietal junction (RTPJ; 49, −49, 23 mm) was positively correlated with RRS scores (depressive, p < 0.001; brooding, p < 0.001; reflective, p = 0.002) in the mood and anxiety disorder group. We then performed a rtfMRI processing simulation to optimize the online computation of the precuneus-RTPJ connectivity. We determined that the two-point method without a control region was appropriate as a functional connectivity neurofeedback signal with less dependence on signal history and its accommodation of head motion. The present study offers a discovery framework for the precise location of functional connectivity targets for rtfMRI-nf intervention, which could help directly translate neuroimaging findings into clinical rtfMRI-nf interventions.

The Functional Segregation and Integration Model: Mixture Model Representations of Consistent and Variable Group-Level Connectivity in fMRI

2016 ◽  
Vol 28 (10) ◽  
pp. 2250-2290 ◽  
Author(s):  
Nathan W. Churchill ◽  
Kristoffer Madsen ◽  
Morten Mørup
Keyword(s):  
Functional Connectivity ◽  
Gaussian Mixture ◽  
Brain Regions ◽  
Model Parameters ◽  
Group Level ◽  
Integration Model ◽  
Connectivity Structure ◽  
Functional Relationships ◽  
Subject Specific ◽  
The Brain

The brain consists of specialized cortical regions that exchange information between each other, reflecting a combination of segregated (local) and integrated (distributed) processes that define brain function. Functional magnetic resonance imaging (fMRI) is widely used to characterize these functional relationships, although it is an ongoing challenge to develop robust, interpretable models for high-dimensional fMRI data. Gaussian mixture models (GMMs) are a powerful tool for parcellating the brain, based on the similarity of voxel time series. However, conventional GMMs have limited parametric flexibility: they only estimate segregated structure and do not model interregional functional connectivity, nor do they account for network variability across voxels or between subjects. To address these issues, this letter develops the functional segregation and integration model (FSIM). This extension of the GMM framework simultaneously estimates spatial clustering and the most consistent group functional connectivity structure. It also explicitly models network variability, based on voxel- and subject-specific network scaling profiles. We compared the FSIM to standard GMM in a predictive cross-validation framework and examined the importance of different model parameters, using both simulated and experimental resting-state data. The reliability of parcellations is not significantly altered by flexibility of the FSIM, whereas voxel- and subject-specific network scaling profiles significantly improve the ability to predict functional connectivity in independent test data. Moreover, the FSIM provides a set of interpretable parameters to characterize both consistent and variable aspects functional connectivity structure. As an example of its utility, we use subject-specific network profiles to identify brain regions where network expression predicts subject age in the experimental data. Thus, the FSIM is effective at summarizing functional connectivity structure in group-level fMRI, with applications in modeling the relationships between network variability and behavioral/demographic variables.

scholarly journals Addressing indirect frequency coupling via partial generalized coherence

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Joseph Young ◽  
Ryota Homma ◽  
Behnaam Aazhang
Keyword(s):  
Functional Connectivity ◽  
Brain Regions ◽  
Partial Coherence ◽  
Future Research ◽  
Partial Frequency ◽  
Model Free ◽  
Frequency Coupling ◽  
Gaussian Case ◽  
Non Gaussian ◽  
Dominant Influence

AbstractDistinguishing between direct and indirect frequency coupling is an important aspect of functional connectivity analyses because this distinction can determine if two brain regions are directly connected. Although partial coherence quantifies partial frequency coupling in the linear Gaussian case, we introduce a general framework that can address even the nonlinear and non-Gaussian case. Our technique, partial generalized coherence (PGC), expands prior work by allowing pairwise frequency coupling analyses to be conditioned on other processes, enabling model-free partial frequency coupling results. By taking advantage of recent advances in conditional mutual information estimation, we are able to implement our technique in a way that scales well with dimensionality, making it possible to condition on many processes and produce a partial frequency coupling graph. We analyzed both linear Gaussian and nonlinear simulated networks. We then performed PGC analysis of calcium recordings from mouse olfactory bulb glomeruli under anesthesia and quantified the dominant influence of breathing-related activity on the pairwise relationships between glomeruli for breathing-related frequencies. Overall, we introduce a technique capable of eliminating indirect frequency coupling in a model-free way, empowering future research to correct for potentially misleading frequency interactions in functional connectivity analyses.

scholarly journals FUNCTIONAL CONNECTIVITY WITHIN LANGUAGE REGIONS IN APHASIC PATIENTS DURING REAL-TIME fMRI-BASED NEUROFEEDBACK TRAINING

2021 ◽  
Author(s):  
Tapasi Brahma ◽  
Chandrasekharan Kesavadas ◽  
PN Sylaja ◽  
Sujesh Sreedharan
Keyword(s):  
Functional Connectivity ◽  
Real Time ◽  
Polar Region ◽  
Test Group ◽  
Brain Regions ◽  
Control Group ◽  
Neurofeedback Training ◽  
Functional Magnetic Resonance ◽  
Training Systems ◽  
The Brain

Stroke is known to disrupt connectivity in the brain in addition to forming scars. This study analyzed the connectivity changes within the language regions and the adjoining brain regions during real-time Functional Magnetic Resonance Imaging-based neurofeedback training for the rehabilitation of stroke-affected patients with expressive aphasia. The study hypothesizes that with repeated sessions of the training, a rise in functional connectivity within the language regions will be observed for the aphasic patients. The experiment was conducted on three groups of subjects: test patients, control patients, and normal participants. Only the test and the normal groups underwent the training. In the training, the subjects exercised language activity covertly to upregulate the Broca’s area. Neurofeedback of the Broca activity (amplified when it is correlated with the Wernicke activity) is visually presented to the subjects to motivate them to improve their performance and stimulate upregulation of functional connectivity of the Broca’s and Wernicke’s areas. The key observations are as follows: For all the groups, a rise in functional connectivity was noticed mostly among the left hemispheric Regions of Interest (ROIs). While comparing the normal group over the test group, ROIs in the frontal polar region were noticed to have good functional connectivity. While comparing the test group over the control group, ROIs in the supra parietal, and the right central opercular regions were found to have good functional connectivity. This study can contribute to the design of rehabilitative training systems that are tuned to activate the regions that have been observed to show increased functional connectivity.

scholarly journals Subject identification using edge-centric functional connectivity

2020 ◽  
Author(s):  
Youngheun Jo ◽  
Joshua Faskowitz ◽  
Farnaz Zamani Esfahlani ◽  
Olaf Sporns ◽  
Richard F. Betzel
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Spatial Scales ◽  
Neural Substrates ◽  
Brain Regions ◽  
Group Level ◽  
Complementary Approach ◽  
Subject Specific ◽  
Subject Identification ◽  
Clinical Conditions

Group-level studies do not capture individual differences in network organization, an important prerequisite for understanding neural substrates shaping behavior and for developing interventions in clinical conditions. Recent studies have employed “fingerprinting” analyses on functional connectivity to identify subjects’ idiosyncratic features. Here, we develop a complementary approach based on an edge-centric model of functional connectivity, which focuses on the co-fluctuations of edges. We first show whole-brain edge functional connectivity (eFC) to be a robust substrate that improves identifiability over nodal FC (nFC) across different datasets and parcellations. Next, we characterized subjects’ identifiability at different spatial scales, from single nodes to the level of functional systems and clusters using k-means clustering. Across spatial scales, we find that heteromodal brain regions exhibit consistently greater identifiability than unimodal, sensorimotor, and limbic regions. Lastly, we show that identifiability can be further improved by reconstructing eFC using specific subsets of its principal components. In summary, our results highlight the utility of the edge-centric network model for capturing meaningful subject-specific features and sets the stage for future investigations into individual differences using edge-centric models.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Real-time Worker Safety Management System Using Deep Learning-based Video Analysis Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 25-30
Author(s):  
So Yeon Jeon ◽  
Jong Hwa Park ◽  
Sang Byung Youn ◽  
Young Soo Kim ◽  
Yong Sung Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Video Analysis ◽  
Management System ◽  
Safety Management ◽  
Worker Safety ◽  
Safety Management System ◽  
Analysis Algorithm

Engaging regularly with fiction influences connectivity in cortical areas for language and mentalizing

2017 ◽  
Author(s):  
Roel M. Willems ◽  
Franziska Hartung
Keyword(s):  
Functional Connectivity ◽  
Time Course ◽  
Language Skills ◽  
Brain Regions ◽  
Brain Areas ◽  
Daily Lives ◽  
Cortical Areas ◽  
Social Abilities ◽  
Behavioral Evidence ◽  
Amount Of Reading

Behavioral evidence suggests that engaging with fiction is positively correlated with social abilities. The rationale behind this link is that engaging with fictional narratives offers a ‘training modus’ for mentalizing and empathizing. We investigated the influence of the amount of reading that participants report doing in their daily lives, on connections between brain areas while they listened to literary narratives. Participants (N=57) listened to two literary narratives while brain activation was measured with fMRI. We computed time-course correlations between brain regions, and compared the correlation values from listening to narratives to listening to reversed speech. The between-region correlations were then related to the amount of fiction that participants read in their daily lives. Our results show that amount of fiction reading is related to functional connectivity in areas known to be involved in language and mentalizing. This suggests that reading fiction influences social cognition as well as language skills.

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