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1. Classification of Pretrial vs. Encoding stage for Working Memory Task among Subjects with pFC Lesions and Healthy Controls using Directed Information

   https://doi.org/10.1109/IEEECONF56349.2022.10052005  

   Balaji, Sai Sanjay; Avvaru, S.V. Sandeep; Parhi, Keshab K. (October 2022, Proc. 2022 Asilomar Conference on Signals, Systems and Computers)

   This paper analyzes the scalp electroencephalogram (EEG) recorded from 14 human subjects with pFC lesions and 20 healthy controls while performing lateral visuospatial working memory tasks to identify the directional brain networks responsible for memory encoding. First, we show that effective connectivity features using directed information (DI) are more accurate and robust than the functional connectivity measure of correlation coefficients in classifying the memory encoding stage from the pretrial phase, with a mean accuracy of 99.36%. Second, we identify the functional segregation of memory encoding to a much smaller sub-network by showing that the top 2.5% of the observed DI features can distinguish memory encoding from the pretrial phase with a mean accuracy of 93.1%. Finally, using graph features, we reveal the increased significance of frontocentral, centroparietal, and temporal regions in memory encoding for subjects with pFC lesions and reduced information flow in the prefrontal, frontal and parietooccipital areas when compared to healthy control. 

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2. Classifying Subjects with PFC Lesions from Healthy Controls during Working Memory Encoding via Graph Convolutional Networks

   https://doi.org/10.1109/NER52421.2023.10123793  

   Balaji, Sai Sanjay; Parhi, Keshab K. (April 2023, 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER))

   This paper describes a group-level classification of 14 patients with prefrontal cortex (pFC) lesions from 20 healthy controls using multi-layer graph convolutional networks (GCN) with features inferred from the scalp EEG recorded from the encoding phase of working memory (WM) trials. We first construct undirected and directed graphs to represent the WM encoding for each trial for each subject using distance correlation- based functional connectivity measures and differential directed information-based effective connectivity measures, respectively. Centrality measures of betweenness centrality, eigenvector centrality, and closeness centrality are inferred for each of the 64 channels from the brain connectivity. Along with the three centrality measures, each graph uses the relative band powers in the five frequency bands - delta, theta, alpha, beta, and gamma- as node features. The summarized graph representation is learned using two layers of GCN followed by mean pooling, and fully connected layers are used for classification. The final class label for a subject is decided using majority voting based on the results from all the subject's trials. The GCN-based model can correctly classify 28 of the 34 subjects (82.35% accuracy) with undirected edges represented by functional connectivity measure of distance correlation and classify all 34 subjects (100% accuracy) with directed edges characterized by effective connectivity measure of differential directed information. 

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3. Graph Convolution Network Based Classification of Subjects with Prefrontal Cortex Lesion via Information-theoretic Brain Network Features

   https://doi.org/10.1007/s11265-025-01944-z  

   Balaji, Sai Sanjay; Parhi, Keshab K (January 2025, Journal of Signal Processing Systems)

   This paper investigates scalp electroencephalogram (EEG) data from 14 subjects with unilateral prefrontal cortex (pFC) lesions and 20 healthy controls during lateral visuospatial working memory (WM) tasks. The goal is to differentiate the brain networks involved in WM processing between these groups. The EEG recordings are transformed into graph signals, with proximity-weighted brain connectivity measures as edges and centrality measures as nodal features. Graph convolutional network (GCN) layers are used for feature representation, followed by a fully connected layer for classification. The GCN-based model effectively handles nine classification tasks, proving that graph-based network representation is versatile for describing brain interactions. The sparse MI-GCI-based graph model’s accuracy effectively captures the functional segregation of distinct WM tasks. The classifier using mutual information-guided Granger causality index (MI-GCI) with 20% of top edges matched prior classification performance with 67% fewer parameters and 80% less graph density, identifying the correct class of all 34 subjects in group identification using leave-one-out cross-validation and two-thirds majority voting. 

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4. Modeling Brain Dynamics During Virtual Reality-Based Emergency Response Learning Under Stress

   https://doi.org/10.1177/00187208211054894  

   Tyagi, Oshin; Hopko, Sarah; Kang, John; Shi, Yangming; Du, Jing; Mehta, Ranjana K. (December 2021, Human Factors: The Journal of the Human Factors and Ergonomics Society)

   Background Stress affects learning during training, and virtual reality (VR) based training systems that manipulate stress can improve retention and retrieval performance for firefighters. Brain imaging using functional Near Infrared Spectroscopy (fNIRS) can facilitate development of VR-based adaptive training systems that can continuously assess the trainee’s states of learning and cognition. Objective The aim of this study was to model the neural dynamics associated with learning and retrieval under stress in a VR-based emergency response training exercise. Methods Forty firefighters underwent an emergency shutdown training in VR and were randomly assigned to either a control or a stress group. The stress group experienced stressors including smoke, fire, and explosions during the familiarization and training phase. Both groups underwent a stress memory retrieval and no-stress memory retrieval condition. Participant’s performance scores, fNIRS-based neural activity, and functional connectivity between the prefrontal cortex (PFC) and motor regions were obtained for the training and retrieval phases. Results The performance scores indicate that the rate of learning was slower in the stress group compared to the control group, but both groups performed similarly during each retrieval condition. Compared to the control group, the stress group exhibited suppressed PFC activation. However, they showed stronger connectivity within the PFC regions during the training and between PFC and motor regions during the retrieval phases. Discussion While stress impaired performance during training, adoption of stress-adaptive neural strategies (i.e., stronger brain connectivity) were associated with comparable performance between the stress and the control groups during the retrieval phase. 

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5. Spectral Features Based Decoding of Task Engagement: The Role of Theta and High Gamma Bands in Cognitive Control

   https://doi.org/10.1109/EMBC46164.2021.9630923  

   Avvaru, Sandeep; Provenza, Nicole R.; Widge, Alik S.; Parhi, Keshab K. (November 2021, Proc. 2021 IEEE Engineering and Medicine in Biology (EMBC) Conference)

   This paper analyzes local field potentials (LFP) from 10 human subjects to discover frequency-dependent biomarkers of cognitive conflict. We utilize cortical and sub-cortical LFP recordings from the subjects during a cognitive task known as the Multi-Source Interference Task (MSIT). We decode the task engagement and discover biomarkers that may facilitate closed-loop neuromodulation to enhance cognitive control. First, we show that spectral power features in predefined frequency bands can be used to classify task and non-task segments with a median accuracy of 88.1%. Here the features are first ranked using the Bayes Factor and then used as inputs to subject-specific linear support vector machine classifiers. Second, we show that theta (4–8 Hz) band, and high gamma (65–200 Hz) band oscillations are modulated during the task performance. Third, by isolating time-series from specific brain regions of interest, we observe that a subset of the dorsolateral prefrontal cortex features is sufficient to decode the task states. The paper shows that cognitive control evokes robust neurological signatures, especially in the prefrontal cortex (PFC). 

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