{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T08:36:23Z","timestamp":1778574983081,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T00:00:00Z","timestamp":1698019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003827","name":"Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund (NKFIH)","doi-asserted-by":"publisher","award":["2019-2.1.11-T\u00c9T-2019-00069"],"award-info":[{"award-number":["2019-2.1.11-T\u00c9T-2019-00069"]}],"id":[{"id":"10.13039\/501100003827","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003827","name":"Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund (NKFIH)","doi-asserted-by":"publisher","award":["101008468"],"award-info":[{"award-number":["101008468"]}],"id":[{"id":"10.13039\/501100003827","id-type":"DOI","asserted-by":"publisher"}]},{"name":"European Union\u2019s Horizon","award":["2019-2.1.11-T\u00c9T-2019-00069"],"award-info":[{"award-number":["2019-2.1.11-T\u00c9T-2019-00069"]}]},{"name":"European Union\u2019s Horizon","award":["101008468"],"award-info":[{"award-number":["101008468"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Time\u2013frequency analysis of EEG data is a key step in exploring the internal activities of the human brain. Studying oscillations is an important part of the analysis, as they are thought to provide the underlying mechanism for communication between neural assemblies. Traditional methods of analysis, such as Short-Time FFT and Wavelet Transforms, are not ideal for this task due to the time\u2013frequency uncertainty principle and their reliance on predefined basis functions. Empirical Mode Decomposition and its variants are more suited to this task as they are able to extract the instantaneous frequency and phase information but are too time consuming for practical use. Our aim was to design and develop a massively parallel and performance-optimized GPU implementation of the Improved Complete Ensemble EMD with the Adaptive Noise (CEEMDAN) algorithm that significantly reduces the computational time (from hours to seconds) of such analysis. The resulting GPU program, which is publicly available, was validated against a MATLAB reference implementation and reached over a 260\u00d7 speedup for actual EEG measurement data, and provided predicted speedups in the range of 3000\u20138300\u00d7 for longer measurements when sufficient memory was available. The significance of our research is that this implementation can enable researchers to perform EMD-based EEG analysis routinely, even for high-density EEG measurements. The program is suitable for execution on desktop, cloud, and supercomputer systems and can be the starting point for future large-scale multi-GPU implementations.<\/jats:p>","DOI":"10.3390\/s23208654","type":"journal-article","created":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T11:39:04Z","timestamp":1698147544000},"page":"8654","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["GPU Implementation of the Improved CEEMDAN Algorithm for Fast and Efficient EEG Time\u2013Frequency Analysis"],"prefix":"10.3390","volume":"23","author":[{"given":"Zeyu","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Information Systems, University of Pannonia, 8200 Veszprem, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0677-8588","authenticated-orcid":false,"given":"Zoltan","family":"Juhasz","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Information Systems, University of Pannonia, 8200 Veszprem, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1926","DOI":"10.1126\/science.1099745","article-title":"Neuronal Oscillations in Cortical Networks","volume":"304","author":"Buzsaki","year":"2004","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"279","DOI":"10.3414\/ME12-01-0083","article-title":"Time-frequency techniques in biomedical signal analysis: A tutorial review of similarities and differences","volume":"52","author":"Wacker","year":"2013","journal-title":"Methods Inf. 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