| Deep learning based on event-related EEG differentiates children with ADHD from healthy controls A Vahid, A Bluschke, V Roessner, S Stober, C Beste Journal of clinical medicine 8 (7), 1055, 2019 | 81 | 2019 |
| Applying deep learning to single-trial EEG data provides evidence for complementary theories on action control A Vahid, M Mückschel, S Stober, AK Stock, C Beste Communications biology 3 (1), 112, 2020 | 68 | 2020 |
| Machine learning provides novel neurophysiological features that predict performance to inhibit automated responses A Vahid, M Mückschel, A Neuhaus, AK Stock, C Beste Scientific reports 8 (1), 16235, 2018 | 36 | 2018 |
| Conditional generative adversarial networks applied to EEG data can inform about the inter-relation of antagonistic behaviors on a neural level A Vahid, M Mückschel, S Stober, AK Stock, C Beste Communications Biology 5 (1), 148, 2022 | 12 | 2022 |
| Classification of alzheimer's disease and mild cognitive impairment: Machine learning applied to rs-fMRI brain graphs S Golbabaei, A Vahid, J Hatami, H Soltanian-Zadeh 2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st …, 2016 | 11 | 2016 |
| Human identification with EEG signals in different emotional states A Vahid, E Arbabi 2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st …, 2016 | 11 | 2016 |
| The intensity of early attentional processing, but not conflict monitoring, determines the size of subliminal response conflicts W Bensmann, A Vahid, C Beste, AK Stock Frontiers in human neuroscience 13, 53, 2019 | 10 | 2019 |
| Training a machine learning classifier to identify ADHD based on real-world clinical data from medical records P Mikolas, A Vahid, F Bernardoni, M Süß, J Martini, C Beste, A Bluschke Scientific Reports 12 (1), 12934, 2022 | 8 | 2022 |
| On the neurophysiological mechanisms underlying the adaptability to varying cognitive control demands N Zink, AK Stock, A Vahid, C Beste Frontiers in Human Neuroscience 12, 411, 2018 | 8 | 2018 |
| Recognizing subjects who are learned how to write with foot from unlearned subjects using EMG signals J Alizadeh, A Vahid, F Bahrami 2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st …, 2016 | 6 | 2016 |
| A data driven machine learning approach to differentiate between autism spectrum disorder and attention-deficit/hyperactivity disorder based on the best-practice diagnostic … N Wolff, G Kohls, JT Mack, A Vahid, EM Elster, S Stroth, L Poustka, ... Scientific Reports 12 (1), 18744, 2022 | 3 | 2022 |
| Applying deep learning to single‑trial EEG data provides evidence for complementary theo‑ries on action control. Commun Biol 3: 112 A Vahid, M Mückschel, S Stober, AK Stock, C Beste | 3 | 2020 |
| On the relative importance of attention and response selection processes for multi-component behavior–Evidence from EEG-based deep learning A Vahid, AK Stock, M Mückschel, C Beste Neuroimage: Reports 2 (3), 100118, 2022 | 2 | 2022 |
| Deep learning on independent spatial EEG activity patterns delineates time windows relevant for response inhibition N Gholamipourbarogh, A Vahid, M Mückschel, C Beste Psychophysiology 60 (10), e14328, 2023 | | 2023 |
| Using Machine Learning Approaches to Predict Controlled Behavior from EEG Data in Humans A Vahid Technische Universität Dresden, 2021 | | 2021 |
Christian Beste, Prof. Dr.TU DresdenBestätigte E-Mail-Adresse bei uniklinikum-dresden.de
