Poornima G J
Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) provide complementary information for investigating brain activity by capturing electrophysiological and hemodynamic responses respectively. However the heterogeneous sampling frequencies and differing physiological characteristics of these modalities present significant challenges for multimodal integration. This study proposes a systematic preprocessing and synchronization framework for multimodal EEG-fNIRS analysis using the recordings from the subjects. Raw fNIRS optical intensity signals acquired at dual wavelengths (780 and 850 nm) were transformed into oxygenated (HbO) and deoxygenated (HbR) hemoglobin concentration changes using the Modified Beer Lambert Law (MBLL). To overcome the disparity in acquisition frequencies a contiguous temporal-bin averaging strategy was employed to generate equal length EEG and fNIRS signals while preserving their temporal characteristics without artificial interpolation. The synchronized recordings obtained from nine participants were subsequently analysed using Pearson Correlation Coefficients to quantify the relationship between neuronal electrical activity and cerebral hemodynamic responses. The experimental results demonstrated predominantly positive correlations across the frontal regions by indicating consistent temporal coupling between EEG and HbO signals while localized variations were observed in the F3 region by reflecting regional neurovascular heterogeneity. The proposed framework effectively synchronizes multimodal brain signals and provides a reliable foundation for subsequent feature extraction, multimodal fusion and machine learning based emotion recognition. The developed preprocessing pipeline contributes toward improving the robustness and physiological interpretability of multimodal brain computer interface and affective computing applications.