Fig. 2

Analysis of predictive frequency bands informed selection of feature space used in the LDA decoder. (a) Power spectral data recorded via sensorimotor channels during right knee extension for a representative participant. Analysis of the power spectral data during movement of pilot participants informed the selection of frequency bands within the feature space. (b) R² scalp topographies for a representative participant. R² was computed between the true movement label, and the power spectrogram was computed for each channel. Pilot data revealed sensorimotor desynchronization in several frequency bands, including µ (8–12 Hz), low β (16–20 Hz), and high β (24–28 Hz). Non-neighboring frequency bands below 30 Hz were selected to prevent overlap in information fed into the decoder and to avoid the stimulation artifact at 30 Hz with the future addition of real-time tSCS. (c) EEG data processing pipeline. EEG data was bandpass 4–40 Hz filtered and common average referenced. Power was extracted in 4 Hz bins by band-passing, squaring, and low-pass filtering the common average referenced data. 480 features were extracted corresponding to 3 frequency bands (µ, low β, and high β), 5 lags, and 32 channels. Lags were incorporated so movement onset predictions can take data from the past 0.5 s into account. (d) Five-fold cross-validation decoder training. The decoder was trained with a 5-fold cross-validation strategy in which four blocks were used as training blocks. Once the hyperparameters were optimized to minimize validation error, the model was retrained on all five blocks and tested on the sixth, unseen block