Causally validated phase-amplitude coupling enables high-fidelity motor decoding for next-generation brain-computer interfaces

Modern Brain-Computer Interfaces (BCIs) face a fundamental performance plateau due to their reliance on broad spectral power reduction (Event-Related Desynchronization, ERD) as the primary decoding feature. ERD acts as a coarse metabolic proxy for cortical activation, discarding the high-frequency temporal syntax necessary for high-dimensional motor control. Here, we demonstrate that Phase-Amplitude Coupling (PAC)--specifically, the phase-locking of high-gamma (70-150 Hz) firing to residual Beta oscillations provides a high-fidelity temporal feature for decoding motor intent. Using high-density electrocorticography (HD-ECoG) during motor imagery, we show that incorporating Beta-PAC into machine-learning classifiers (LDA/SVM) significantly outperforms traditional power-based features. To definitively validate the causal robustness of this feature against mathematically-derived artifacts, we leveraged a rare in vivo human structural lesion model. In a patient with focal tumor infiltratio