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1. EEG-derived Brain Connectivity in Theta/Alpha Frequency Bands Increases During Reading of Individual Words

   https://doi.org/10.1101/2025.03.10.642502  

   Delavari, Fatemeh; Ekves, Zachary; Hancock, Roeland; Altmann, Gerry; Santaniello, Sabato (March 2025, bioRxiv)

   Objective: Although extensive insights about the neural mechanisms of reading have been gained via magnetic and electrographic imaging, the temporal evolution of the brain network during sight reading remains unclear. We tested whether the temporal dynamics of the brain functional connectivity involved in sight reading can be tracked using high-density scalp EEG recordings. Approach: Twenty-eight healthy subjects were asked to read words in rapid serial visual presentation task while recording scalp EEG, and phase locking value was used to estimate the functional connectivity between EEG channels in the theta, alpha, beta, and gamma frequency bands. The resultant networks were then tracked through time. Main results: The network's graph density gradually increases as the task unfolds, peaks 150-250-ms after the appearance of each word, and returns to resting-state values, while the shortest path length between non-adjacent functional areas decreases as the density increases, thus indicating that a progressive integration between regions can be detected at the scalp level. This pattern was independent of the word's type or position in the sentence, occurred in the theta/alpha band but not in beta/gamma range, and peaked earlier in the alpha band compared to the theta band (alpha: 184 ± 61.48-ms; theta: 237 ± 65.32-ms, P-value P<0.01). Nodes in occipital and frontal regions had the highest eigenvector centrality throughout the word's presentation, and no significant lead-lag relationship between frontal/occipital regions and parietal/temporal regions was found, which indicates a consistent pattern in information flow. In the source space, this pattern was driven by a cluster of nodes linked to sensorimotor processing, memory, and semantic integration, with the most central regions being similar across subjects. Significance: These findings indicate that the brain network connectivity can be tracked via scalp EEG as reading unfolds, and EEG-retrieved networks follow highly repetitive patterns lateralized to frontal/occipital areas during reading. 

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2. Characterization of the Stages of Creative Writing With Mobile EEG Using Generalized Partial Directed Coherence

   https://doi.org/10.3389/fnhum.2020.577651  

   Cruz-Garza, Jesus G.; Sujatha Ravindran, Akshay; Kopteva, Anastasiya E.; Rivera Garza, Cristina; Contreras-Vidal, Jose L. (December 2020, Frontiers in Human Neuroscience)

   null (Ed.)

   Two stages of the creative writing process were characterized through mobile scalp electroencephalography (EEG) in a 16-week creative writing workshop. Portable dry EEG systems (four channels: TP09, AF07, AF08, TP10) with synchronized head acceleration, video recordings, and journal entries, recorded mobile brain-body activity of Spanish heritage students. Each student's brain-body activity was recorded as they experienced spaces in Houston, Texas (“Preparation” stage), and while they worked on their creative texts (“Generation” stage). We used Generalized Partial Directed Coherence (gPDC) to compare the functional connectivity among both stages. There was a trend of higher gPDC in the Preparation stage from right temporo-parietal (TP10) to left anterior-frontal (AF07) brain scalp areas within 1–50 Hz, not reaching statistical significance. The opposite directionality was found for the Generation stage, with statistical significant differences ( p < 0.05) restricted to the delta band (1–4 Hz). There was statistically higher gPDC observed for the inter-hemispheric connections AF07–AF08 in the delta and theta bands (1–8 Hz), and AF08 to TP09 in the alpha and beta (8–30 Hz) bands. The left anterior-frontal (AF07) recordings showed higher power localized to the gamma band (32–50 Hz) for the Generation stage. An ancillary analysis of Sample Entropy did not show significant difference. The information transfer from anterior-frontal to temporal-parietal areas of the scalp may reflect multisensory interpretation during the Preparation stage, while brain signals originating at temporal-parietal toward frontal locations during the Generation stage may reflect the final decision making process to translate the multisensory experience into a creative text. 

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3. 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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4. Predicting Treatment Outcome in Spinal Cord Stimulation with EEG

   See, Kyle; July, Rachel; Coombes, Stephen; Fang, Ruogu (October 2021, Biomedical Engineering Society Annual Meeting)

   Introduction: Back pain is one of the most common causes of pain in the United States. Spinal cord stimulation (SCS) is an intervention for patients with chronic back pain (CBP). However, SCS decreases pain in only 58% of patients and relies on self-reported pain scores as outcome measures. An SCS trial is temporarily implanted for seven days and helps to determine if a permanent SCS is needed. Patients that have a >50% reduction in pain from the trial stimulator makes them eligible for permanent implantation. However, self-reported measures reveal little on how mechanisms in the brain are altered. Other measurements of pain intensity, onset, medication, disabilities, depression, and anxiety have been used with machine learning to predict outcomes with accuracies <70%. We aim to predict long-term SCS responders at 6-months using baseline resting EEG and machine learning. Materials and Methods: We obtained 10-minutes of resting electroencephalography (EEG) and pain questionnaires from nine participants with CBP at two time points: 1) pre-trial baseline. 2) Six months after SCS permanent implant surgery. Subjects were designated as high or moderate responders based on the amount of pain relief provided by the long-term (post six months) SCS, and pain scored on a scale of 0-10 with 0 being no pain and 10 intolerable. We used the resting EEG from baseline to predict long-term treatment outcome. Resting EEG data was fed through a pipeline for classification and to map dipole sources. EEG signals were preprocessed using the EEGLAB toolbox. Independent component analysis and dipole fitting were used to linearly unmix the signal and to map dipole sources from the brain. Spectral analysis was performed to obtain the frequency distribution of the signal. Each power band, delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-100 Hz), as well as the entire spectrum (1-100 Hz), were used for classification. Furthermore, dipole sources were ranked based on classification feature weights to determine the significance of specific regions in the brain. We used support vector machines to predict pain outcomes. Results and Discussion: We found higher frequency powerbands provide overall classification performance of 88.89%. Differences in power are seen between moderate and high responders in both the frontal and parietal regions for theta, alpha, beta, and the entire spectrum (Fig.1). This can potentially be used to predict patient response to SCS. Conclusions: We found evidence of decreased power in theta, alpha, beta, and entire spectrum in the anterior regions of the parietal cortex and posterior regions of the frontal cortex between moderate and high responders, which can be used for predicting treatment outcomes in long-term pain relief from SCS. Long-term treatment outcome prediction using baseline EEG data has the potential to contribute to decision making in terms of permanent surgery, forgo trial periods, and improve clinical efficiency by beginning to understand the mechanism of action of SCS in the human brain. 

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5. 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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