Ridge EEG diagnostic figures#

Status: schematic demo figures, not benchmark evidence.

Prediction source: schematic recomputed linear_ridge on synthetic demo data.

Critical interpretation note#

The repo’s current linear_ridge baseline fits a ridge model after reshaping [windows, time, channels] into [windows*time, channels]. It therefore learns a regularized channel-to-channel linear map, not a full time-by-channel flattened-window model. High performance should be interpreted as evidence that near-future EEG is linearly predictable from recent channel covariance/temporal continuity under the current split, not as proof of a rich neural state model.

Generated files#

  • fig1_ridge_input_target_waveforms.png

  • fig2_ridge_prediction_overlay.png

  • fig3_autocorrelation_lag_structure.png

  • fig4_ridge_coefficient_channel_map.png

  • fig5_psd_residual_diagnostics.png

Metrics from plotted prediction#

  • global_mse: 0.213588

  • global_mae: 0.37722

  • global_pearsonr: 0.655473

  • n_train_windows: 320

  • n_test_windows: 80

  • window_timepoints: 128

  • n_channels: 8

  • sfreq: 128

Provenance#

  • source: synthetic schematic generated by plotting script

Optional outputs skipped#

  • skipped topomap: schematic channels do not have valid sensor positions