Brain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices by interpreting neural signals. BCIs can utilize both invasive signals, such as electrocorticogram (ECoG), and non-invasive signals, like electroencephalogram (EEG). This work aims to compare EEG and ECoG signals during repeated finger flexion-extension. First, it examines CorticoKinematic Coherence (CKC) at low frequencies, analyzing Low Motor Potentials (LMPs). Then, it assesses neural decoding for BCIs by predicting finger trajectories, comparing LMPs and higher frequency bands, as well as a basic (Multiple Linear Regressor, MLR) and a more complex model (Temporal Convolutional Network, TCN). ECoG data comes from the publicly available Stanford ECoG dataset, which includes recordings from nine subjects performing repeated self-paced, single-finger flexion-extension.