{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multi-modal-spectral-parametrization-method","title":"Multi-Modal Spectral Parametrization Method (MMSPM) for analyzing EEG activity with distinct scaling regimes","arxiv_id":"2505.18117","date":"2025-05-23","proceeding":null,"authors":["Frigyes Samuel Racz","John Milton","Juan Luis Cabrera","Gábor Csukly","José del R. Millán"],"abstract":"Aperiodic neural activity has been the subject of intense research interest lately as it could reflect on the cortical excitation/inhibition ratio, which is suspected to be affected in numerous clinical conditions. This phenomenon is characterized via the aperiodic scaling exponent $\\beta$, equal to the spectral slope following log-log transformation of power spectra. Despite recent progress, however, most current methods do not take into consideration the plausible multimodal nature in the power spectra of neurophysiological recordings - i.e., $\\beta$ might be different in low- ($\\beta_{lo}$) and high-frequency ($\\beta_{hi}$) regimes -, especially in case of $|\\beta_{lo}|>|\\beta_{hi}|$. Here we propose an algorithm, the multi-modal spectral parametrization method (MMSPM) that aims to account for this issue. MMSPM estimates $\\beta_{lo}$ and $\\beta_{hi}$ separately using a constrained, piece-wise regression technique, and also assesses if they are significantly different or instead the spectrum is indeed unimodal and can be characterized simply with broadband $\\beta$. Here we present the MMSPM algorithm and evaluate its performance in silico on simulated power spectra. Then, we use MMSPM on resting-state electroencephalography (EEG) data collected from 19 young, healthy volunteers, as well as on a separate dataset of EEG recordings from 30 schizophrenia patients and 31 healthy controls, and demonstrate that broadband (0.1-100 Hz and 0.5-45 Hz) EEG spectra can indeed present a bimodality pattern with significantly steeper low-range ($<\\sim2$ Hz) and flatter high-range scaling regimes (i.e., $|\\beta_{lo}|>|\\beta_{hi}|$). Clinical relevance: The MMSPM method characterizes aperiodic neural activity in distinct scaling regimes, which can be relevant in numerous pathological conditions such as dementia or schizophrenia.","url_abs":"https://arxiv.org/abs/2505.18117v2","url_pdf":"https://arxiv.org/pdf/2505.18117v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multi-modal-spectral-parametrization-method","repo_url":"https://github.com/samuelracz/MMSPM","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"eeg-1","task_name":"EEG"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}