Corticokinematic coherence (CKC) is the phase coupling between limb kinematics and cortical neurophysiological signals, reflecting cortical processing of proprioceptive afference, and it is reproducible when estimated with magnetoencephalography (MEG). However, feasibility and reproducibility of CKC based on electroencephalography (EEG) is still unclear and is the primary object of the present report. Thirteen healthy right-handed volunteers (seven females, 21.7±4.3 yr) participated in two combined MEG/EEG sessions 12.6±1.3 mo apart. Participants' dominant and nondominant index finger was continuously moved at 3 Hz for 4 min separately using a pneumatic-movement actuator. Coherence was computed between finger acceleration and three derivations of EEG signals: 1) average reference, 2) bipolar derivations, and 3) surface Laplacian. CKC strength was defined as the peak coherence value at movement frequency. Intraclass-correlation coefficient values (0.74-0.93) indicated excellent intersession reproducibility for CKC strength for all derivations and moved fingers. CKC strength obtained with EEG was approximately two times lower compared with MEG, but the values were positively correlated across the participants. CKC strength was significantly (P < 0.01) higher for bipolar (session 1: 0.19±0.09; session 2: 0.20±0.10) and surface Laplacian (session 1: 0.22± 0.09; session 2: 0.21±0.09) derivations than for the average reference (session 1: 0.10±0.04; session 2: 0.11±0.05). We demonstrated that CKC is a feasible and reproducible tool to monitor proprioception using EEG recordings, although the strength of CKC was twice lower for EEG compared with MEG. Laplacian and bipolar (CP3-C1/CP3-C3 and CP4-C2/C4-FC2) EEG derivation(s) are recommended for future research and clinical use of CKC method. NEW & NOTEWORTHY The most important message in this report is that the corticokinematic coherence (CKC) method is a feasible and reproducible tool to quantify, map, and follow cortical proprioceptive (“the movement sense”) processing using EEG that is more widely available for CKC recordings than previously used magnetoencephalography designs, in basic research, but especially in clinical environments. We provide useful recommendations for optimal EEG derivations for cost-effective experimental designs, making it possible to scale up in sample size in future studies.