{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T10:58:52Z","timestamp":1773053932063,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T00:00:00Z","timestamp":1772841600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"PIUNT E701"},{"name":"Argentinian \u201cUniversidad Nacional de Tucum\u00e1n\u201d"},{"name":"Argentinian \u201cConsejo Nacional de Investigaciones Cient\u00edficas y T\u00e9cnicas\u201d"},{"name":"Argentinian \u201cInstituto Superior de Investigaciones Biol\u00f3gicas\u201d"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Time\u2013frequency (TF) characterization of electromyographic (EMG) bursts is essential for accurately assessing muscle function, particularly when the signals exhibit a high degree of nonstationarity. In this exploratory study, we investigated the temporal dynamics of the spectral components associated with short-latency EMG bursts using several TF analysis techniques. Specifically, we compared the performance and interpretability of spectrograms obtained via the short-time Fourier transform (STFT), the continuous wavelet transform (CWT), and noise-assisted multivariate empirical mode decomposition (NA-MEMD), applied to EMG signals recorded from the biceps femoris muscle of freely moving rats in an animal model of Parkinson\u2019s disease, acquired using chronically implanted bipolar electrodes during treadmill locomotion. For each method, we evaluated its effectiveness in capturing transient variations in frequency content, the stability of extracted features across bursts, and the extent to which these features reflect physiologically meaningful aspects of muscle activation. The results show that TF approaches reveal complementary information about burst structure; NA-MEMD provides greater adaptability to nonlinear and nonstationary components, whereas STFT- and CWT-based representations offer more controlled and comparable analyses. Overall, these findings highlight the value of TF analysis as a methodological tool for evaluating muscle function and provide a solid foundation for selecting analytical strategies in studies where EMG bursts exhibit complex and highly variable spectral profiles.<\/jats:p>","DOI":"10.3390\/s26051688","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T08:58:45Z","timestamp":1773046725000},"page":"1688","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Assessing Time\u2013Frequency Analysis Methods for Non-Stationary EMG Bursts: Application to an Animal Model of Parkinson\u2019s Disease"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8836-7517","authenticated-orcid":false,"given":"Fernando Daniel","family":"Farf\u00e1n","sequence":"first","affiliation":[{"name":"Neuroscience and Applied Technologies Laboratory (LINTEC), Bioengineering Department, Faculty of Exact Sciences and Technology (FACET), National University of Tucuman, Superior Institute of Biological Research (INSIBIO), National Scientific and Technical Research Council (CONICET), Av. Independencia 1800, San Miguel de Tucuman 4000, Argentina"},{"name":"Institute of Bioengineering, Universidad Miguel Hern\u00e1ndez of Elche, 03202 Elche, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1001-8672","authenticated-orcid":false,"given":"Ana L\u00eda","family":"Albarrac\u00edn","sequence":"additional","affiliation":[{"name":"Neuroscience and Applied Technologies Laboratory (LINTEC), Bioengineering Department, Faculty of Exact Sciences and Technology (FACET), National University of Tucuman, Superior Institute of Biological Research (INSIBIO), National Scientific and Technical Research Council (CONICET), Av. Independencia 1800, San Miguel de Tucuman 4000, Argentina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5891-111X","authenticated-orcid":false,"given":"Leonardo Ariel","family":"Cano","sequence":"additional","affiliation":[{"name":"Neuroscience and Applied Technologies Laboratory (LINTEC), Bioengineering Department, Faculty of Exact Sciences and Technology (FACET), National University of Tucuman, Superior Institute of Biological Research (INSIBIO), National Scientific and Technical Research Council (CONICET), Av. Independencia 1800, San Miguel de Tucuman 4000, Argentina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7052-6011","authenticated-orcid":false,"given":"Eduardo","family":"Fern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Institute of Bioengineering, Universidad Miguel Hern\u00e1ndez of Elche, 03202 Elche, Spain"},{"name":"Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28029 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1109\/TIM.2016.2534378","article-title":"Probability Density Functions of Stationary Surface EMG Signals in Noisy Environments","volume":"65","author":"Thongpanja","year":"2016","journal-title":"IEEE Trans. Instrum. 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