Stevie Foglia

Xiru Wang, Aiden Li, Hongzhao Tan et al.

Chronic neck pain is a leading cause of disability worldwide, and current treatment selection remains largely trial and error. We present a machine learning framework that uses electroencephalography to predict treatment efficacy in patients with chronic neck pain, with the goal of supporting individualized therapy and reducing the burden on healthcare systems. The framework centers on a rigorous data preprocessing stage tailored to the characteristics of each EEG recording type. For resting-state EEG, the preprocessing pipeline comprises baseline signal removal, bad channel identification and exclusion, re-referencing, bandpass and notch filtering, Independent Component Analysis, and power spectral density analysis. For motor execution and motor imagery recordings, the same initial steps are applied, after which signals are aligned to trigger events so that event-related desynchronization (ERD) and event-related synchronization (ERS) can be quantified. Synchronously recorded electromyography data are bandpass filtered and smoothed with a moving average, then correlated with the corresponding EEG channels to characterize the EEG EMG relationship during attempted movement. In parallel, we performed an extensive literature review of machine learning models applied to clinical EEG (763 records initially screened, 16 patient and 47 healthy-control studies retained), to inform the post-processing strategy. Through this combined preprocessing and review effort, we aim to develop a robust predictive model that can support personalized healthcare strategies in chronic pain management.

Zhen Tang, Ameer Hamoodi, Stevie Foglia et al.

This research addresses a validated TMS EEG cleaning pipeline and a corresponding benchmark dataset. It evaluates two widely used artifact removal pipelines. A reference dataset of carefully preprocessed EEG signals was established to support future algorithm development and enable systematic comparison of automated artifact removal strategies, despite the absence of a true physiological ground truth. The study evaluates the effectiveness of two widely used source based artifact removal approaches and examines their impact on signal quality improvement and preservation of TMS-evoked potentials. The results support the robustness of the proposed preprocessing workflow and demonstrate its potential for improving data reliability in both research and clinical applications. A key goal is integrating TMS EEG and embedding it within a larger BCI framework. Ultimately, these efforts aim to enhance understanding of cortical dynamics and expand the clinical and research applications of TMS EEG.

Siqi Zhao, Wangyang Li, Xiru Wang et al.

Machine learning (ML) and deep learning (DL) techniques have been widely applied to analyze electroencephalography (EEG) signals for disease diagnosis and brain-computer interfaces (BCI). The integration of multimodal data has been shown to enhance the accuracy of ML and DL models. Combining EEG with other modalities can improve clinical decision-making by addressing complex tasks in clinical populations. This systematic literature review explores the use of multimodal EEG data in ML and DL models for clinical applications. A comprehensive search was conducted across PubMed, Web of Science, and Google Scholar, yielding 16 relevant studies after three rounds of filtering. These studies demonstrate the application of multimodal EEG data in addressing clinical challenges, including neuropsychiatric disorders, neurological conditions (e.g., seizure detection), neurodevelopmental disorders (e.g., autism spectrum disorder), and sleep stage classification. Data fusion occurred at three levels: signal, feature, and decision levels. The most commonly used ML models were support vector machines (SVM) and decision trees. Notably, 11 out of the 16 studies reported improvements in model accuracy with multimodal EEG data. This review highlights the potential of multimodal EEG-based ML models in enhancing clinical diagnostics and problem-solving.