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The Effect of tDCS on EEG-Based Functional Connectivity in Gait Motor Imagery., , , , , and . IWINAC (1), volume 11486 of Lecture Notes in Computer Science, page 3-10. Springer, (2019)Using EEG Signals to Detect Different Surfaces While Walking., , , , and . IWINAC (2), volume 10338 of Lecture Notes in Computer Science, page 109-114. Springer, (2017)Comparison of different EEG signal analysis techniques for an offline lower limb motor imagery brain-computer interface., , , and . EMBC, page 203-206. IEEE, (2018)Analyzing electrode configurations to detect intention of pedaling initiation through EEG signals., , , and . SMC, page 1614-1619. IEEE, (2016)Personalized Offline and Pseudo-Online BCI Models to Detect Pedaling Intent., , , and . Front. Neuroinform., (2017)Classification of Gait Motor Imagery While Standing Based on Electroencephalographic Bandpower., , , and . IWINAC (2), volume 10338 of Lecture Notes in Computer Science, page 61-67. Springer, (2017)Novel tDCS montage favors lower limb motor imagery detection., , , and . EMBC, page 2170-2173. IEEE, (2018)Effect on the classification of motor imagery in EEG after applying anodal tDCS with a 4×1 ring montage over the motor cortex., , , , and . ICORR, page 818-822. IEEE, (2017)Detection of intention of pedaling start cycle through EEG signals., , , , , and . EMBC, page 1496-1499. IEEE, (2016)