Introduction: Photobiomodulation (PBM) is emerging as a non-invasive neuromodulation modality with applications in neurological, psychiatric, and performance domains. However, most PBM protocols are currently applied in fixed, open-loop configurations that do not account for individual variability in brain dynamics. Advances in artificial intelligence (AI), EEG analytics, and computational neuroscience enable a shift toward personalized, data-driven neuromodulation. This workshop introduces an integrated framework combining AI-guided EEG analysis, photobiomodulation, and control-theoretic modeling for adaptive brain stimulation.

Methods: Workshop Design: Interactive workshop combining didactic instruction, case-based discussion, and live demonstration of AI-integrated PBM systems. Core Components:

1. EEG-Based Brain-State Assessment •Acquisition and preprocessing of EEG signals
   • Feature extraction using signal processing and machine learning
   • Identification of biomarkers and network patterns for clinical interpretation
2. AI-Guided Personalization •Mapping EEG-derived features to individualized PBM parameters
   • Translation of neural signatures into stimulation protocols
   • Use of normative EEG databases for benchmarking and personalization
3. Control-Theoretic Framework (BiCE) •Introduction to Bidirectional Control Energy (BiCE) as a quantitative measure of brain-state efficiency
   • Directional components: oKc (subcortical → cortical) oKs (cortical → subcortical)
   • Interpretation of control energy as a marker of network stability and efficiency
   • Conceptual application of BiCE in guiding neuromodulation strategies
4. Adaptive and Closed-Loop Neuromodulation
   • Framework for integrating EEG monitoring with real-time parameter adjustment
   • Conceptual workflow: odata acquisition → state estimation → control energy computation → parameter optimization
   • Discussion of closed-loop PBM and hybrid neuromodulation systems

• Device Specifications (demonstration systems):
  • Manufacturer: Vielight Inc., Toronto, Canada
  • Models and Platform: Neuro Pro, Vielight AI
  • Wavelengths: 810 nm, 1064 nm
  • Power: ~25–300 mW per emitter
  • Mode: Pulsed (10 Hz, 40 Hz, and variable protocols)
  • Duty Cycle: ~50% •Probe Design: oIntranasal applicator oTranscranial LED clusters
  • Power Density: ~25–300 mW/cm² (depending on locations of LEDs)
  • Energy Density: ~15–180 J/cm²
  • Total Energy: ~1100 J per session
• Parameters reflect clinically relevant human PBM protocols used for demonstration and training purposes.

Results (Workshop Outcomes): Participants will:

• Understand how EEG-derived biomarkers can inform PBM parameter selection
• Learn how AI methods enable translation from brain-state assessment to personalized stimulation protocols
• Gain familiarity with control-theoretic concepts, including BiCE, as tools for quantifying brain-state efficiency
• Explore the potential of adaptive and closed-loop PBM systems for improving neuromodulation outcomes
• Develop practical skills in applying PBM using integrated EEG and AI-guided approaches

Conclusions: The integration of AI, EEG analytics, and control-theoretic modeling represents a significant advancement in the evolution of photobiomodulation from fixed protocols to personalized and adaptive neuromodulation. This workshop presents a practical framework for implementing these approaches in clinical and research settings, with potential applications across neurological disorders, mental health, and performance optimization. The convergence of PBM with data-driven and control-based methodologies may enable more precise and effective modulation of brain-state dynamics.

Learning Objectives:

• Apply EEG-based assessment for personalized PBM: Interpret EEG-derived biomarkers and network features to inform individualized photobiomodulation parameter selection.
• Translate AI-driven analytics into neuromodulation protocols: Explain how machine learning and normative EEG frameworks can be used to map brain-state characteristics to personalized stimulation strategies
• Understand control-theoretic modeling of brain-state dynamics: Describe the principles of EEG monitoring, AI, and control-based frameworks for real-time, adaptive photobiomodulation in clinical and performance applications.

Introduction: Neurodegenerative diseases are increasingly recognized as systemic network disorders involving the gut–brain axis, glymphatic clearance system, and chronic neuroinflammation — not isolated brain failures. Current PBM approaches predominantly use transcranial or site-specific irradiation, which cannot reach the gut–brain axis, directly activate glymphatic drainage, or resolve systemic inflammation. This workshop presents a paradigm shift: whole-body PBM (WB-PBM) as a simultaneous, multi-axis preventive strategy targeting the gut, brain, and sleep–glymphatic system within a single session.

Methods: This 3-hour interactive workshop combines didactic presentation with a live physiological demonstration. Didactic content covers the mechanistic rationale for WB-PBM across three axes: 

1. Gut–brain axis pathology (Braak’s hypothesis, mapranosis, LPS-driven neuroinflammation)
2. Glymphatic system dysfunction (AQP4-mediated clearance, sleep architecture)
3. The triple synergy of WB-PBM (20-minute physiological cascade)

• Device: Hue Light USA Whole-Body PBM Chamber, Model 03.
• Manufacturer: Hue Light USA, New York, USA. Device type: LED. Number of diodes: 38,880 medical-grade.
• Wavelengths: 530 (green), 660 (red), 850 (near-infrared), 940 nm (infrared).
• Irradiance: 17–25 mW/cm².
• Mode: continuous wave and pulsed (Nogier Frequencies, 1–5000 Hz).
• Spot size: whole-body chamber (full-body exposure including abdominal region).
• Treatment time: 20 minutes, single session. Total dose in 10 min: 50 J/cm². FDA registered. 

Parameters provided by manufacturer. Supporting clinical evidence is drawn from a pilot RCT (NCT07271927) at Pusan National University Yangsan Hospital (Korea), approved by Korean MFDS (No. 1730, May 2024) and IRB No. 23-2024-005: 14 PD patients, 10 weeks, 3×/week, 30 sessions × 20 minutes.

Results: Pilot RCT results demonstrated statistically significant improvements in UPDRS Part I (mentation/behavior/mood, p = 0.008, r = −0.71), Seoul Verbal Learning Test immediate recall (p = 0.009), and Rey Complex Figure Test immediate (p = 0.035) and delayed recall (p = 0.039). Motor scores were maintained. Zero serious adverse events. Natural deep sleep onset observed at 7–10 minutes. The live demonstration is expected to show acute HRV increase, stress index decrease, improved vascular compliance, and increased prefrontal oxygenation — confirming simultaneous network activation.

Conclusions: WB-PBM simultaneously engages the gut–brain axis, glymphatic clearance, and systemic inflammation resolution — a network solution for a network problem. Clinical evidence from PD provides proof-of-mechanism; the broader implication is a preventive intervention applicable before established pathology. This workshop equips practitioners with mechanistic understanding and real-time physiological evidence to integrate WB-PBM into prevention-oriented clinical practice.

Declaration Statement: The pilot RCT received IRB approval (No. 23-2024-005) and Korean MFDS regulatory approval (No. 1730). Informed patient consent was obtained. The presenter discloses a consulting relationship with BrainTap and commercial involvement with BioTekna assessment devices.

Disclosure Statement: Assessment equipment (PPG Stress Flow) provided by BioTekna. WB-PBM device provided by Hue Light USA. No external funding was received for this workshop.

Learning Objectives:

• Explain the mechanistic rationale for whole-body PBM as a simultaneous intervention targeting the gut-brain axis, glymphatic system, and systemic inflammation in neurodegeneration prevention.
• Interpret real-time PPG-based HRV and HEG-based prefrontal cortex assessment data to evaluate acute physiological responses to whole-body PBM.
• Appraise the clinical evidence from the first pilot RCT of whole-body PBM in Parkinson's disease and apply its implications to prevention-orientated practice.