Decoding Imagined, Heard, and Spoken Speech: Classification and Regression of EEG Using a 14-Channel Dry-Contact Mobile Headset. J Clayton, S Wellington, C Valentini-Botinhao, O Watts INTERSPEECH, 4886-4890, 2020 | 14 | 2020 |
Decoding continuous kinetic information of grasp from stereo-electroencephalographic (SEEG) recordings X Wu, G Li, S Jiang, S Wellington, S Liu, Z Wu, B Metcalfe, L Chen, ... Journal of Neural Engineering 19 (2), 026047, 2022 | 9 | 2022 |
Fourteen-channel EEG with Imagined Speech (FEIS) dataset S Wellington, J Clayton University of Edinburgh, 2019 | 6 | 2019 |
Bimodal electroencephalography-functional magnetic resonance imaging dataset for inner-speech recognition F Simistira Liwicki, V Gupta, R Saini, K De, N Abid, S Rakesh, ... Scientific Data 10 (1), 378, 2023 | 4* | 2023 |
Speech decoding from stereo-electroencephalography (sEEG) signals using advanced deep learning methods X Wu, S Wellington, Z Fu, D Zhang Journal of Neural Engineering, 2024 | 2 | 2024 |
Bimodal electroencephalography-functional magnetic resonance imaging dataset for inner-speech recognition FS Liwicki, V Gupta, R Saini, K De, N Abid, S Rakesh, S Wellington, ... Scientific Data 10 (1), 378, 2023 | 1 | 2023 |
Performance of data-driven inner speech decoding with same-task EEG-fMRI data fusion and bimodal models H Wilson, S Wellington, FS Liwicki, V Gupta, R Saini, K De, N Abid, ... arXiv preprint arXiv:2306.10854, 2023 | 1 | 2023 |
Bimodal pilot study on inner speech decoding reveals the potential of combining EEG and fMRI F Simistira Liwicki, V Gupta, R Saini, K De, N Abid, S Rakesh, ... | | 2022 |