Neurodesign of Motor Intention: Frequency–Amplitude–Power Signatures of Mu (8-13 Hz) and Beta (13-30 Hz) Rhythms for Enhanced Decoding of Human Movement Intention

Rodrigo Monteiro Sobreira; David Uzêda; Guillaume Réveillère

doi:10.34178/jbth.v9i4.614

Rodrigo Monteiro Sobreira
David Uzêda
Guillaume Réveillère

DOI: https://doi.org/10.34178/jbth.v9i4.614

Keywords: Beta Bursts, Brain-Computer Interface, Electroencephalography, Event-Related Desynchronization

Abstract

Sensorimotor Mu (8–13 Hz) and Beta (13–30 Hz) rhythms constitute robust electrophysiological fingerprints of cortical dynamics accompanying the planning, execution and cessation of voluntary movement. To clarify how these spectral signatures can be harnessed in non-invasive brain–computer interfaces (BCIs) for neuro-adaptive feedback systems, a systematic review of studies published between January 2015 and July 2025 was undertaken (in addiction to others to elucidate clinical and technical concepts). Four databases (PubMed, Scopus, Embase and IEEE Xplore) were searched for adult EEG research addressing motor preparation, execution, imagery or action observation. After the eligibility screening, 35 articles satisfied all inclusion criteria. Most experiments employed, at least, thirty-two scalp electrodes 0.5–40 Hz, band-pass filtering and artefact rejection via independent component analysis. Across protocols, contralateral Mu/Beta power fell (event-related desynchronisation) over sensorimotor cortex during real or imagined movement, followed by a rapid Beta rebound (event-related synchronisation) signalling cortical re-inhibition. Peak Mu (~10 Hz) and Beta (~20 Hz) frequencies varied modestly among participants, indicating that individual calibration can enhance single-trial classification accuracy. Transient Beta bursts lasting under 200 ms consistently marked movement termination, whereas stronger Mu suppression correlated with superior performance for neurofeedback of post-stroke rehabilitation tasks. Several studies also reported task-dependent Beta–Gamma coupling and attentional modulation of Mu amplitude as emerging control variables. By mapping where, when and how strongly these rhythms fluctuate, the review delineates clear feature-selection targets and adaptive-threshold guidelines for next-generation BCIs aimed at motor learning and recovery.