FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4206556457> ?p ?o ?g. }

• W4206556457 abstract "As a measure of the brain's electrical activity, electroencephalography (EEG) is the primary signal of interest for brain-computer-interfaces (BCI). BCIs offer a communication pathway between a brain and an external device, translating thought into action with suitable processing. EEG data is the most common signal source for such technologies. However, artefacts induced in BCIs in the real-world context can severely degrade their performance relative to their in-laboratory performance. In most cases, the recorded signals are so heavily corrupted by noise that they are unusable and restrict BCI's broader applicability. To realise the use of portable BCIs capable of high-quality performance in a real-world setting, we use Generative Adversarial Networks (GANs) that can adopt both supervised and unsupervised learning approaches. Although our approach is supervised, the same model can be used for unsupervised tasks such as data augmentation/imputation in the low resource setting. Exploiting recent advancements in Generative Adversarial Networks (GAN), we construct a pipeline capable of denoising artefacts from EEG time series data. In the case of denoising data, it maps noisy EEG signals to clean EEG signals, given the nature of the respective artefact. We demonstrate the capability of our network on a toy dataset and a benchmark EEG dataset developed explicitly for deep learning denoising techniques. Our datasets consist of an artificially added mains noise (50/60 Hz) artefact dataset and an open-source EEG benchmark dataset with two artificially added artefacts. Artificially inducing myogenic and ocular artefacts for the benchmark dataset allows us to present qualitative and quantitative evidence of the GANs denoising capabilities and rank it among the current gold standard deep learning EEG denoising techniques. We show the power spectral density (PSD), signal-to-noise ratio (SNR), and other classical time series similarity measures for quantitative metrics and compare our model to those previously used in the literature. To our knowledge, this framework is the first example of a GAN capable of EEG artefact removal and generalisable to more than one artefact type. Our model has provided a competitive performance in advancing the state-of-the-art deep learning EEG denoising techniques. Furthermore, given the integration of AI into wearable technology, our method would allow for portable EEG devices with less noisy and more stable brain signals." @default.
• W4206556457 created "2022-01-26" @default.
• W4206556457 creator A5020118136 @default.
• W4206556457 creator A5030133086 @default.
• W4206556457 creator A5064593698 @default.
• W4206556457 creator A5080429518 @default.
• W4206556457 creator A5084014715 @default.
• W4206556457 creator A5088955239 @default.
• W4206556457 date "2022-01-13" @default.
• W4206556457 modified "2023-10-04" @default.
• W4206556457 title "Denoising EEG Signals for Real-World BCI Applications Using GANs" @default.
• W4206556457 cites W2009021023 @default.
• W4206556457 cites W2024461528 @default.
• W4206556457 cites W2151669316 @default.
• W4206556457 cites W2162800060 @default.
• W4206556457 cites W2776446922 @default.
• W4206556457 cites W2797212135 @default.
• W4206556457 cites W2808618759 @default.
• W4206556457 cites W2898664946 @default.
• W4206556457 cites W2915196348 @default.
• W4206556457 cites W2972411444 @default.
• W4206556457 cites W3000242789 @default.
• W4206556457 cites W3080551539 @default.
• W4206556457 cites W3161343527 @default.
• W4206556457 cites W3202417708 @default.
• W4206556457 doi "https://doi.org/10.3389/fnrgo.2021.805573" @default.
• W4206556457 hasPublicationYear "2022" @default.
• W4206556457 type Work @default.
• W4206556457 citedByCount "4" @default.
• W4206556457 countsByYear W42065564572022 @default.
• W4206556457 countsByYear W42065564572023 @default.
• W4206556457 crossrefType "journal-article" @default.
• W4206556457 hasAuthorship W4206556457A5020118136 @default.
• W4206556457 hasAuthorship W4206556457A5030133086 @default.
• W4206556457 hasAuthorship W4206556457A5064593698 @default.
• W4206556457 hasAuthorship W4206556457A5080429518 @default.
• W4206556457 hasAuthorship W4206556457A5084014715 @default.
• W4206556457 hasAuthorship W4206556457A5088955239 @default.
• W4206556457 hasBestOaLocation W42065564571 @default.
• W4206556457 hasConcept C115961682 @default.
• W4206556457 hasConcept C118552586 @default.
• W4206556457 hasConcept C119857082 @default.
• W4206556457 hasConcept C13280743 @default.
• W4206556457 hasConcept C151730666 @default.
• W4206556457 hasConcept C153180895 @default.
• W4206556457 hasConcept C154945302 @default.
• W4206556457 hasConcept C15744967 @default.
• W4206556457 hasConcept C163294075 @default.
• W4206556457 hasConcept C173201364 @default.
• W4206556457 hasConcept C185798385 @default.
• W4206556457 hasConcept C199360897 @default.
• W4206556457 hasConcept C205649164 @default.
• W4206556457 hasConcept C2779343474 @default.
• W4206556457 hasConcept C41008148 @default.
• W4206556457 hasConcept C43521106 @default.
• W4206556457 hasConcept C522805319 @default.
• W4206556457 hasConcept C86803240 @default.
• W4206556457 hasConcept C99498987 @default.
• W4206556457 hasConceptScore W4206556457C115961682 @default.
• W4206556457 hasConceptScore W4206556457C118552586 @default.
• W4206556457 hasConceptScore W4206556457C119857082 @default.
• W4206556457 hasConceptScore W4206556457C13280743 @default.
• W4206556457 hasConceptScore W4206556457C151730666 @default.
• W4206556457 hasConceptScore W4206556457C153180895 @default.
• W4206556457 hasConceptScore W4206556457C154945302 @default.
• W4206556457 hasConceptScore W4206556457C15744967 @default.
• W4206556457 hasConceptScore W4206556457C163294075 @default.
• W4206556457 hasConceptScore W4206556457C173201364 @default.
• W4206556457 hasConceptScore W4206556457C185798385 @default.
• W4206556457 hasConceptScore W4206556457C199360897 @default.
• W4206556457 hasConceptScore W4206556457C205649164 @default.
• W4206556457 hasConceptScore W4206556457C2779343474 @default.
• W4206556457 hasConceptScore W4206556457C41008148 @default.
• W4206556457 hasConceptScore W4206556457C43521106 @default.
• W4206556457 hasConceptScore W4206556457C522805319 @default.
• W4206556457 hasConceptScore W4206556457C86803240 @default.
• W4206556457 hasConceptScore W4206556457C99498987 @default.
• W4206556457 hasFunder F4320320847 @default.
• W4206556457 hasLocation W42065564571 @default.
• W4206556457 hasLocation W42065564572 @default.
• W4206556457 hasLocation W42065564573 @default.
• W4206556457 hasOpenAccess W4206556457 @default.
• W4206556457 hasPrimaryLocation W42065564571 @default.
• W4206556457 hasRelatedWork W2019731069 @default.
• W4206556457 hasRelatedWork W2159786295 @default.
• W4206556457 hasRelatedWork W2542551374 @default.
• W4206556457 hasRelatedWork W2895843840 @default.
• W4206556457 hasRelatedWork W3004117467 @default.
• W4206556457 hasRelatedWork W3123920941 @default.
• W4206556457 hasRelatedWork W3180487986 @default.
• W4206556457 hasRelatedWork W4307930854 @default.
• W4206556457 hasRelatedWork W4386127803 @default.
• W4206556457 hasRelatedWork W2092619848 @default.
• W4206556457 hasVolume "2" @default.
• W4206556457 isParatext "false" @default.
• W4206556457 isRetracted "false" @default.
• W4206556457 workType "article" @default.