{
  "abstract": "Non-invasive brain-computer interfaces (BCIs) for lower-limb exoskeleton control suffer from severe non-stationarity during prolonged ambulation, driven by central motor fatigue and electrode impedance drift. Standard classifiers relying solely on tonic primary motor cortex (M1) band power degrade rapidly as task duration extends. Here, we introduce a two-dimensional (2D) dynamical phase-space decoding framework, [W_P(t), dW_P(t)/dt], termed the Predictive Adaptive Temporal Tracking Unit (PATTU), which combines an autoregressive source-space intention state (W_P(t)) with its first-order phasic intention derivative (dW_P(t)/dt) reconstructed via anatomically constrained standardized low-resolution brain electromagnetic tomography (sLORETA). Evaluated across cross-session motor imagery (EEGBCI, N=10) and prolonged lower-limb exoskeleton ambulation (ds007788, N=7), adaptive phase-space boundary tracking restores cross-session classification accuracy from 55.87% to 65.51% (+9.64%, p = 0.0078) and achieves a cohort mean held-out ROC-AUC of 0.6730 during active exoskeleton control (peaking at 0.8257 in high-responder trials, with adaptive thresholding recovering accuracy from 53.76% to 73.76% in drift-affected runs). Longitudinal source-space tracking across 40-minute exoskeleton sessions reveals that while tonic paracentral M1 source power erodes under fatigue (declining in 5 of 7 subjects; -2.30% in sub-01), the phasic intention derivative remains resilient (1.202 s⁻¹ in early alert runs vs. 1.413 s⁻¹ in late fatigued runs; 119.5% mean retention), accompanied by a +150.0 ms relative within-subject onset latency shift (45.7 ms early vs. 195.7 ms late). Cortical surface mapping across six bilateral networks demonstrates that the central nervous system preserves phasic initiation velocity by dynamically reallocating relative source power from fatigued paracentral M1 generators toward prefrontal executive (+3.59%), supplementary motor, and primary somatosensory (S1, +1.34%) hubs.",
  "abstract_shown": true,
  "affiliations": [
    "Independent Researcher, Tirunelveli, Tamil Nadu, India"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Nikil Shajan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/e970qpm7n53dv.bib",
  "cited_by": 0,
  "doi": "10.5281/zenodo.23479039",
  "fulltext_url": null,
  "id": "e970qpm7n53dv",
  "issns": [],
  "landing_url": "https://zenodo.org/records/23479039",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": "https://zenodo.org/api/records/23479039/files/Project_1%20(10).pdf/content",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/e970qpm7n53dv.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:20:59Z",
      "native_id": "23479039",
      "source": "zenodo",
      "url": "https://zenodo.org/records/23479039"
    }
  ],
  "subjects": [],
  "title": "Phasic Intention Derivative Resilience and Multi-Lobe Cortical Reallocation During Prolonged Lower-Limb Exoskeleton Control: A 2D Source-Space Phase-Space Framework",
  "title_text": "Phasic Intention Derivative Resilience and Multi-Lobe Cortical Reallocation During Prolonged Lower-Limb Exoskeleton Control: A 2D Source-Space Phase-Space Framework",
  "type": "preprint",
  "updated_at": "2026-11-02T12:20:59Z",
  "url": "https://research.draconitic.com/paper/e970qpm7n53dv",
  "venue": "Zenodo",
  "versions": []
}