{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:56:39Z","timestamp":1760230599762,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,8,4]],"date-time":"2022-08-04T00:00:00Z","timestamp":1659571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Brain\u2013computer interfaces are an emerging field of medical technology that enable users to control external digital devices via brain activity. Steady-state evoked potential is a type of electroencephalogram signal that is widely used for brain\u2013computer interface applications. Collecting electroencephalogram data is an effort-intensive task that requires technical expertise, specialised equipment, and ethical considerations. This work proposes a class-conditioned Wasserstein generative adversarial network with a gradient penalty loss for electroencephalogram data generation. Electroencephalogram data were recorded via a g.tec HiAmp using 5, 6, 7.5, and 10 Hz flashing video stimuli. The resulting model replicates the key steady-state-evoked potential features after training for 100 epochs with 25 batches of 4 s steady-state-evoked potential data. This creates a model that mimics brain activity, producing a type of symmetry between the brain\u2019s visual reaction to frequency-based stimuli as measured by electroencephalogram and the model output.<\/jats:p>","DOI":"10.3390\/sym14081600","type":"journal-article","created":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T02:12:39Z","timestamp":1659665559000},"page":"1600","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Neurocartographer: CC-WGAN Based SSVEP Data Generation to Produce a Model toward Symmetrical Behaviour to the Human Brain"],"prefix":"10.3390","volume":"14","author":[{"given":"Sefa E.","family":"Karabulut","sequence":"first","affiliation":[{"name":"Biomedical Engineering Research Group, School of Electrical & Information Engineering, University of the Witwatersrand, Johannesburg, 1 Jan Smuts Avenue, Braamfontein, Johannesburg 2000, South Africa"}]},{"given":"Mohammad Mehdi","family":"Khorasani","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Research Group, School of Electrical & Information Engineering, University of the Witwatersrand, Johannesburg, 1 Jan Smuts Avenue, Braamfontein, Johannesburg 2000, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4080-6389","authenticated-orcid":false,"given":"Adam","family":"Pantanowitz","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Research Group, School of Electrical & Information Engineering, University of the Witwatersrand, Johannesburg, 1 Jan Smuts Avenue, Braamfontein, Johannesburg 2000, South Africa"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.eij.2015.06.002","article-title":"Brain computer interfacing: Applications and challenges","volume":"16","author":"Abdulkader","year":"2015","journal-title":"Egypt. 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