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1. Population-based spectral characteristics of normal interictal scalp EEG inform diagnosis and treatment planning in focal epilepsy

   https://doi.org/10.1038/s41598-025-08871-w  

   Wagh, Neeraj; Duque-Lopez, Andrea; Joseph, Boney; Berry, Brent; Jehi, Lara; Crepeau, Daniel; Barnard, Leland; Gogineni, Venkatsampath; Brinkmann, Benjamin H; Jones, David T; et al (December 2025, Scientific Reports)

   Normal routine electroencephalograms (EEGs) can cause delays in the diagnosis and treatment of epilepsy, especially in drug-resistant patients and those without structural abnormalities. There is a need for alternative quantitative approaches that can inform clinical decisions when traditional visual EEG review is inconclusive. We leverage a large population EEG database (N = 13,652 recordings, 12,134 unique patients) and an independent cohort of patients with focal epilepsy (N = 121) to investigate whether normal EEG segments could support the diagnosis of focal epilepsy. We decomposed expertly graded normal EEGs (N = 6,242) using unsupervised tensor decomposition to extract the dominant spatio-spectral patterns present in a clinical population. We then, using the independent cohort of patients with focal epilepsy, evaluated whether pattern loadings of normal interictal EEG segments could classify focal epilepsy, the epileptogenic lobe, presence of lesions, and drug response. We obtained six physiological patterns of EEG spectral power and connectivity with distinct spatio-spectral signatures. Both pattern types together effectively differentiated patients with focal epilepsy from non-epileptic controls (mean AUC 0.78) but failed to classify the epileptogenic lobe. Spectral power-based patterns best classified drug-resistant epilepsy (mean AUC 0.73) and lesional epilepsy (mean AUC 0.67), albeit with high variability across patients. Our findings support that visibly normal patient EEGs contain subtle quantitative differences of clinical relevance. Further development may yield normal EEG-based computational biomarkers that can augment traditional EEG review and epilepsy care. 

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2. EEG-derived brain connectivity in theta/alpha frequency bands increases during reading of individual words

   https://doi.org/10.1007/s11571-025-10280-8  

   Delavari, Fatemeh; Ekves, Zachary; Hancock, Roeland; Altmann, Gerry_T M; Santaniello, Sabato (December 2025, Cognitive Neurodynamics)

   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 a 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 band, 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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3. 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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4. Brain interactions underlying speech monitoring in language production

   https://doi.org/10.1016/j.jneuroling.2025.101285  

   Wang, Yusheng; Andrade, Katherine D; Anderson, Elizabeth J; Kansal, Leena; McDonald, Carrie; Ben-Haim, Sharona; Shih, Jerry; Ashrafi, Ashkan; Riès, Stephanie K (November 2025, Journal of Neurolinguistics)

   This study investigates functional connectivity patterns between brain regions supporting speech monitoring in seven participants with epilepsy undergoing stereotactic electroencephalographic (SEEG) monitoring and participating in a picture-word interference (PWI) task. Using graph signal processing (GSP) techniques, we found that three brain regions previously associated with speech monitoring processes, namely the anterior cingulate cortex (ACC), superior temporal gyrus (STG), and insula, functionally interact with each other and other regions around vocal onset and that these connections are more probable in errors than correct trials. Furthermore, the timing of their involvement in functional connectivity metrics indicates that they play distinct yet complementary roles in inner and outer speech monitoring. Specifically, the ACC maintains constant connectivity patterns before and after vocal onset, suggesting a continuous role in proactive and reactive speech monitoring. The STG shows stronger connectivity after vocal onset, consistent with its role in processing auditory feedback and external monitoring. Conversely, the insula exhibits stronger connectivity before vocal onset than after, suggesting that it plays a role in articulatory planning and inner speech monitoring. Our results align with the conflict-based account of speech monitoring in language production and suggest that inner and outer speech monitoring are supported by dynamic interactions between key brain regions of a network. 

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5. Diagnosis of Pediatrics Epilepsy Based on Graph analysis of Scalp EEG Applying Mutual Information

   https://doi.org/10.1109/CSCI58124.2022.00322  

   Zarafshan, Elaheh; Forouzannezhad, Parisa; Rajaei, Hoda; Morar, Ulyana; Cabrerizo, Mercedes; Yaylali, Ilker; Pinzon, Alberto; Adjouadi, Malek (December 2022, IEEE)

   Epilepsy is a brain disorder that causes seizures, affecting nearly half a million children in the US alone. In this study, we aimed to use a nonlinear driven method to characterize scalp EEG recordings of pediatric epilepsy patients (PE: n=7) compared to pediatric control subjects (PC: n=7) in a clinical environment. A time-varying approach was used to construct functional connectivity networks (FCNs) of all subjects. Next, the FCNs are mapped into the form of undirected graphs that are subjected to the extraction of graph theory-based features. An unsupervised clustering technique based on K-mean is used to delineate the PE from the PC group. Our findings show a statistically significant difference in the mean FCNs between PC and PE groups (t(340)=- 15.9899, p<< 0.0001). Performance results showed an accuracy of 92.5% with a sensitivity of 90% and a specificity of 95.3%. This approach can help improve and validate the early diagnosis of PE by applying non-invasive scalp EEG signals. 

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