en Cognitive Neuroscience Cognitive Neuroscience Original Original <div style="text-align: justify;"><span style="font-size:12pt"><span new="" roman="" style="font-family:" times="">Accurate localization of neural activity from electroencephalography (EEG) remains challenging due to the ill‑posed inverse problem and spatial leakage between cortical regions. Although several linear inverse methods have been proposed, their practical performance under realistic experimental conditions is not fully understood. This study compares four widely used distributed inverse solutions, minimum norm estimation (MNE), dynamic statistical parametric mapping (dSPM), standardized low‑resolution electromagnetic tomography (sLORETA), and exact LORETA (eLORETA), focusing on spatial resolution and leakage characteristics.</span></span><br> <span style="font-size:12pt"><span new="" roman="" style="font-family:" times="">EEG data were collected from nineteen right‑handed participants (mean age 23 &plusmn; 3.5 years) performing two visual tasks: face detection and motion discrimination. Recordings were obtained using a 128‑channel EEG system at 2048 Hz. Spatial resolution was assessed using peak localization error (PLE) and spatial dispersion (SD) derived from point spread functions (PSF) and cross‑talk functions (CTF). Spatial leakage patterns were also examined across functionally relevant cortical networks.</span></span><br> <span style="font-size:12pt"><span new="" roman="" style="font-family:" times="">Statistical comparisons revealed significant differences between inverse methods for PSF based localization accuracy, with sLORETA and eLORETA achieving near zero localization error, whereas MNE and dSPM showed larger deviations. However, under CTF analyses reflecting more realistic multi‑source activity, all methods exhibited increased localization errors and similar spatial spread. Network analyses further indicated that anatomical organization influences leakage patterns, with compact regions such as V1 demonstrating higher localization specificity than distributed visual networks.</span></span><br> <span style="font-size:12pt"><span new="" roman="" style="font-family:" times="">Overall, the results indicate that no single inverse method optimizes all aspects of spatial resolution. Instead, each method reflects a trade‑off between focality, smoothness, and robustness, highlighting the importance of selecting inverse models according to the goals of EEG source analysis.</span></span></div> EEG source localization, Inverse problem, Minimum norm estimation (MNE), sLORETA / eLORETA, dynamic statistical parametric mapping (dSPM) 0 0 http://bcn.iums.ac.ir/browse.php?a_code=A-10-8573-1&slc_lang=en&sid=1 Mohammad Ahadzadeh 13700319475328460059210 13700319475328460059210 No Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran Amir Hosein Ardalan 13700319475328460059211 13700319475328460059211 No Sharayan Intelligent Process and Analysis Research and Development Team, Tehran, Iran Reyhaneh Amiri 13700319475328460059212 13700319475328460059212 No Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran Arefeh Rezagholi 13700319475328460059213 13700319475328460059213 No Faculty of Computer and Electrical Engineering, Tarbiat Modares University, Tehran, Iran Golnaz Baghdadi golnaz_baghdadi@aut.ac.ir 13700319475328460059214 13700319475328460059214 Yes Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran