Gas Phase Electrophotonic Analysis: Full-Body Biofield Mapping from Ten Fingertips

Language: en

Ten Windows into the Whole

There is an old principle in holographic science: every fragment of a hologram contains information about the entire image. Cut a hologram in half, and each half still shows the complete picture — just at lower resolution. This principle, it turns out, applies to biological systems as well. The fingertips, the ears, the feet, the irises of the eyes — each contains a microsystem representation of the entire body. Stimulate the right point on the ear, and you can treat a knee. Read the patterns in the iris, and you can assess the liver.

This principle — that the whole is encoded in each part — is the foundation of Gas Phase Electrophotonic (GPE) analysis, the most advanced form of biofield assessment technology currently available. By scanning all ten fingertips using electrophotonic imaging and mapping each sector of each fingertip to a specific organ system, GPE analysis constructs a full-body biofield map from 20 seconds of data collection.

The technology is an evolution of Gas Discharge Visualization (GDV), developed by physicist Konstantin Korotkov at Saint Petersburg’s ITMO University. While basic GDV captures and quantifies the gas discharge image from individual fingertips, GPE analysis goes further — it integrates data from all ten fingers into a comprehensive assessment of the body’s energetic state, mapping to Traditional Chinese Medicine (TCM) meridian systems, the chakra system of yogic anatomy, and modern organ physiology.

The Technical Architecture

The GPE system consists of three components: a hardware device for image capture, a software platform for analysis, and a mapping system that translates image data into biofield assessments.

The Capture Device

The current generation of Korotkov’s instrument — variously called the GDV Camera, Bio-Well, or EPC (Electro-Photonic Capture) device — works as follows:

The subject places a fingertip on a glass electrode plate. A precisely controlled electromagnetic pulse (10 kV amplitude, 1024 Hz frequency, 10 microsecond pulse duration) is applied to the electrode. This pulse creates an intense but brief electric field that extracts electrons from the fingertip surface, initiating a gas discharge in the thin air gap between the finger and the glass.

The resulting gas discharge — a corona of ionized gas — emits visible light in characteristic patterns determined by the electrical and physical properties of the fingertip surface. A CCD camera positioned behind the glass electrode captures this luminous corona as a digital image.

The entire scan takes approximately 1 second per fingertip. A complete scan of all ten fingers (performed twice — once without a filter and once with a thin polymer filter placed over the glass) takes approximately 20-30 seconds.

The filter scan is significant. Without the filter, the gas discharge is influenced by both the electrical properties of the skin (which reflect autonomic nervous system activity, sweat gland function, and blood flow) and by the gas environment near the finger surface. With the filter, the direct electrical contribution is attenuated, and the image more strongly reflects the gas composition near the skin — which is influenced by volatile organic compounds, water vapor, and other gases emitted by the body.

Comparing the filtered and unfiltered scans provides two complementary views of the biofield:

• Unfiltered scan: Reflects the current psycho-emotional state, autonomic nervous system activity, and acute physiological conditions.
• Filtered scan: Reflects the underlying constitutional state, chronic conditions, and deeper organ function.

The Software Analysis

The captured images are processed by proprietary software that extracts multiple parameters from each fingertip image:

Glow area. The total size of the gas discharge corona, measured in pixel units. This reflects the overall energy emission from the fingertip and, by extension, the energy level of the corresponding organ systems.

Glow intensity. The average brightness of the corona, reflecting the density of the gas discharge and the strength of the bioelectric signal.

Entropy. A measure of the disorder or randomness in the glow pattern. Healthy systems show moderate entropy — not too ordered (rigid, suppressed) and not too chaotic (dysregulated, stressed).

Fractality. The fractal dimension of the glow boundary — a measure of its complexity. Healthy biofields produce fractal boundaries in a specific range. Deviations indicate either suppression (too smooth) or hyperactivation (too jagged).

Emission gaps (lacunae). Dark areas or breaks in the corona where the discharge is absent or significantly reduced. These gaps are interpreted as areas of energy deficiency in the corresponding organ or system.

Density variations. Bright spots, dark spots, and density gradients within the corona that provide information about the distribution of bioelectric activity across the fingertip surface.

The Mapping System

This is where GPE analysis makes its most ambitious — and most controversial — claim: that specific sectors of each fingertip’s corona correspond to specific organs and organ systems.

The mapping is based on two complementary systems:

TCM meridian theory. Traditional Chinese Medicine holds that the body’s vital energy (qi) flows through 12 primary meridians, each associated with a specific organ. Six of these meridians terminate at the fingertips — the lung, large intestine, pericardium (circulation-sex), triple warmer (endocrine), heart, and small intestine meridians. The location of each meridian endpoint on the finger determines which sector of the GDV image corresponds to which organ system.

Su Jok correspondence therapy. Developed by Korean professor Park Jae Woo, Su Jok therapy maps the entire body onto the hand and fingers using a detailed microsystem correspondence. Each finger represents specific body regions and organ systems. The Su Jok mapping provides a more granular sector assignment than the TCM meridian map alone.

Korotkov and his team spent over a decade refining this mapping by correlating GDV sector analysis with clinical diagnoses in thousands of patients. They found statistically significant correlations between GDV sector parameters and clinically confirmed conditions in the corresponding organs — establishing an empirical basis for the mapping system independent of its theoretical foundations in TCM and Su Jok.

The Full-Body Biofield Image

The most visually striking output of GPE analysis is the full-body biofield image — a human silhouette surrounded by a computer-generated energy field based on the aggregated data from all ten fingertip scans.

This image is not a photograph of the aura. It is a data visualization — a way of rendering the quantitative parameters from the fingertip scans into an intuitive visual representation. The biofield image shows:

Overall field size. The larger the biofield image, the higher the overall energy level. A significantly contracted field suggests depletion, illness, or emotional suppression. An abnormally expanded field can indicate hyperactivation, inflammation, or manic states.

Field symmetry. The left and right sides of the biofield image should be approximately symmetric. Significant asymmetry indicates autonomic imbalance, unilateral pathology, or energetic disturbance.

Field continuity. The biofield should form a continuous envelope around the body silhouette. Gaps, indentations, or protrusions indicate areas of energy deficiency or excess in the corresponding body regions.

Color coding. Some software versions use color to indicate intensity, stress level, or deviation from normal ranges — warmer colors for excess, cooler colors for deficiency.

Chakra alignment. The software extracts data from specific fingertip sectors corresponding to the seven major chakras and displays their relative activation levels. This provides a quantitative assessment of the chakra system — a concept traditionally assessed only through subjective perception by healers and clairvoyants.

Correlation Studies: What the Research Shows

Korotkov and colleagues have published numerous studies correlating GPE analysis with clinical measures:

Organ-Sector Correlations

A series of studies at Russian medical institutions compared GPE sector analysis with clinical diagnoses in patients with known conditions:

Cardiovascular disease. Patients with documented coronary artery disease, hypertension, and heart failure showed significantly altered GPE parameters in the sectors corresponding to the heart and cardiovascular system. The sectors mapped to the heart (on the left ring finger and right little finger) showed reduced glow area, increased entropy, and characteristic gap patterns.

Gastrointestinal disease. Patients with gastritis, peptic ulcer disease, and inflammatory bowel disease showed alterations in the GI-mapped sectors (primarily on the middle fingers). The alterations were proportional to disease severity and improved with successful treatment.

Respiratory disease. Asthma, chronic bronchitis, and COPD patients showed changes in the lung-mapped sectors (primarily on the thumbs). The changes included reduced glow area and increased emission gaps.

Endocrine disorders. Thyroid disease, diabetes, and adrenal insufficiency showed characteristic patterns in the endocrine-mapped sectors.

Mental health. Depression was associated with overall reduction in glow area and increased symmetry deviation. Anxiety was associated with increased entropy and erratic glow patterns. PTSD showed distinctive patterns with specific sector involvement.

The correlations were statistically significant but not diagnostically specific — meaning that GPE analysis could identify that something was abnormal in a particular organ system but could not reliably distinguish between different conditions within that system (for example, it could detect a gastrointestinal problem but not reliably distinguish between gastritis and ulcerative colitis).

TCM Meridian Validation

Several studies have investigated the correspondence between GPE sector analysis and TCM diagnostic assessment by experienced acupuncturists:

Bundzen et al. (2002) compared GPE analysis with pulse diagnosis and tongue diagnosis in 87 patients. The agreement between GPE sector analysis and TCM diagnosis was 73-85% depending on the meridian, suggesting a substantial but imperfect correspondence.

Korotkov et al. (2008) compared GPE analysis with Ryodoraku — an electrical conductance measurement of acupuncture points used in Japanese medicine for meridian assessment. The correlation between GPE sector parameters and Ryodoraku readings for the corresponding meridians was significant (r = 0.45-0.68), providing independent validation of the GPE mapping system.

Chakra Assessment

The chakra analysis module of GPE software extracts data from specific fingertip sectors mapped to each of the seven major chakras. Validation studies have compared these readings with:

Subjective assessments by experienced healers. In blind protocols, the correlation between GPE chakra readings and healer assessments ranged from moderate to good (kappa = 0.4-0.7), with the highest agreement for extreme states (very open or very blocked chakras) and lower agreement for intermediate states.

Psychological assessments. Studies have found correlations between GPE chakra readings and psychological questionnaires measuring constructs associated with each chakra — security (root), creativity (sacral), personal power (solar plexus), emotional openness (heart), communication (throat), intuition (third eye), and spiritual connection (crown).

Clinical Protocols: How Practitioners Use GPE

Initial Assessment

A typical GPE assessment session proceeds as follows:

1. Preparation. The client washes and dries their hands. Jewelry is removed. The room temperature is noted.
2. Baseline scan. All ten fingertips are scanned in sequence (right thumb through right little finger, then left thumb through left little finger), without filter and then with filter.
3. Computer analysis. The software processes the images and generates the biofield image, organ analysis, chakra analysis, and energy balance metrics.
4. Interpretation. The practitioner reviews the data with the client, identifying areas of strength and concern, comparing the filtered and unfiltered scans, and correlating findings with the client’s health history and presenting concerns.
5. Recommendations. Based on the analysis, the practitioner may recommend specific interventions — meditation, breathwork, acupuncture, dietary changes, or other therapies — targeted at the identified areas of imbalance.

Treatment Monitoring

GPE analysis is particularly valuable for monitoring treatment effects in real time:

Before and after acupuncture. Scans taken before and after acupuncture sessions show changes in the specific sectors corresponding to the treated meridians — providing objective evidence of treatment effect.

Before and after meditation. Scans taken before and after meditation sessions typically show increased overall glow area, improved symmetry, and reduced emission gaps — providing objective feedback to the meditator about the effectiveness of their practice.

Before and after energy healing. Scans before and after Reiki, Therapeutic Touch, or Pranic Healing sessions document changes in both the practitioner’s and the client’s biofield — often showing complementary changes (the healer’s field expanding and intensifying while the client’s field becomes more balanced and coherent).

Treatment tracking over time. Serial GPE scans over weeks or months of treatment show progressive changes that correlate with clinical improvement — providing a longitudinal biofield record that tracks the trajectory of healing.

Population Studies

GPE has been used in large-scale studies to assess the biofield characteristics of different populations:

Athletes. Studies of competitive athletes show characteristic biofield patterns — generally larger glow area, better symmetry, and higher entropy (reflecting greater adaptive capacity). Overtraining produces a distinctive pattern of reduced glow area and increased asymmetry.

Meditators. Long-term meditators show larger baseline glow area, better symmetry, and more stable biofield parameters compared to non-meditators. The differences are proportional to meditation experience (years of practice).

Cancer patients. Studies at oncology clinics found that cancer patients showed significantly reduced overall glow area and specific sector deficits corresponding to the organ system affected by the cancer. The GPE findings correlated with disease staging and treatment response.

Psychiatric patients. Patients with depression, anxiety, schizophrenia, and bipolar disorder showed distinctive GPE patterns, with the most severe cases showing the most extreme deviations from normal.

The Biofield as Data Stream

The engineering metaphor illuminates what GPE analysis actually does: it treats the human biofield as a data stream and applies signal processing techniques to extract meaningful information.

Each fingertip scan is a data acquisition event — 30,000 to 100,000 pixels of image data, each encoding information about the gas discharge characteristics at that spatial location. The total data set from a complete 20-finger scan (10 unfiltered + 10 filtered) is approximately 1-2 million data points.

The software processes this data through a series of algorithms:

1. Image segmentation. Separating the gas discharge corona from the background.
2. Sector assignment. Dividing each corona into sectors based on the organ mapping system.
3. Feature extraction. Computing the glow area, intensity, entropy, fractality, and gap characteristics for each sector.
4. Cross-correlation. Comparing filtered and unfiltered scans to separate acute from chronic patterns.
5. Normalization. Comparing the extracted features against population norms (age, sex, time of day).
6. Visualization. Rendering the processed data as the full-body biofield image, organ maps, and chakra displays.

This is, in essence, the same signal processing pipeline used in any modern diagnostic imaging system — from MRI to CT to ultrasound. The difference is the input signal (gas discharge rather than magnetic resonance or X-ray absorption) and the mapping system (TCM meridians rather than Western anatomy).

The question is not whether this pipeline produces data. It clearly does — the images are real, the parameters are quantifiable, and the statistical correlations with clinical conditions are documented. The question is whether the data is specific enough, validated enough, and clinically reliable enough to serve as a primary diagnostic tool.

The honest answer, as of the current state of research, is: not yet for primary diagnosis in Western medical practice, but valuable as a complementary assessment tool, a treatment monitoring system, a research instrument, and a biofeedback device for consciousness training.

The Frontier: AI-Enhanced GPE Analysis

The next generation of GPE analysis is incorporating artificial intelligence — machine learning algorithms trained on thousands of GPE scans with known clinical outcomes.

Early results are promising. Neural networks trained on GPE images can:

• Classify health states with accuracy exceeding traditional sector analysis, because the neural network identifies patterns in the raw image data that the human eye and traditional algorithms miss.
• Predict treatment outcomes based on pre-treatment GPE patterns — identifying which patients are likely to respond to specific interventions.
• Detect subtle changes that precede clinical symptoms — potentially providing earlier detection of emerging health conditions.
• Integrate longitudinal data from multiple scans over time, identifying trajectory patterns that predict health outcomes months in advance.

The combination of GPE imaging with AI analysis represents a convergence of ancient observation (the body’s energy field), modern physics (gas discharge visualization), and cutting-edge computation (deep learning). It is, in many ways, the future of biofield assessment — a future where the wisdom of traditional energy medicine is operationalized through engineering and amplified by artificial intelligence.

Limitations and the Path Forward

GPE analysis faces several significant limitations:

Standardization. Despite Korotkov’s efforts to standardize the technology, there is still variability between devices, software versions, and scanning protocols. International standards for GPE assessment are needed.

Independent validation. Much of the validation research has been conducted by Korotkov’s group or close collaborators. More independent replication by unaffiliated research teams is needed to strengthen the evidence base.

Mechanistic understanding. The precise biophysical mechanism by which systemic organ conditions influence the gas discharge at specific fingertip sectors remains incompletely understood. The TCM meridian and Su Jok microsystem theories provide a framework, but a complete mechanistic model is lacking.

Clinical utility. GPE analysis has not been validated for specific clinical diagnoses in randomized controlled trials meeting the standards required by Western regulatory agencies (FDA, CE marking for diagnostics). Its role is currently complementary rather than primary.

Practitioner training. Interpretation of GPE data requires training in both the technical aspects of the system and the clinical correlations. Untrained users may draw incorrect conclusions from the data.

Despite these limitations, GPE analysis represents the most comprehensive attempt to date to quantify the human biofield using reproducible physical measurements. It provides a bridge between the subjective perception of traditional healers and the quantitative demands of modern science — a bridge that, while still under construction, is increasingly passable in both directions.

The ten fingertips, it turns out, are ten windows. And through them, with the right instrument and the right analysis, you can see the whole.

References and Further Reading

Korotkov, K. G. (2002). Human Energy Field: Study with GDV Bioelectrography. Backbone Publishing.

Korotkov, K. G. (2014). Energy Fields Electrophotonic Analysis in Humans and Nature. CreateSpace.

Korotkov, K. G., Matravers, P., Orlov, D., & Williams, B. (2010). Application of electrophoton capture (EPC) analysis based on gas discharge visualization (GDV) technique in medicine: A systematic review. Journal of Alternative and Complementary Medicine, 16(1), 13-25.

Bundzen, P. V., Korotkov, K. G., & Unestahl, L. E. (2002). Altered states of consciousness: Review of experimental data obtained with a multiple techniques approach. Journal of Alternative and Complementary Medicine, 8(2), 153-165.

Alexandrova, R., Fedoseev, G., Korotkov, K., et al. (2003). Analysis of the bioelectrograms of bronchial asthma patients. In Measuring Energy Fields: State of the Science (pp. 70-82). Backbone Publishing.

Park, J. W. (1993). Su Jok Acupuncture. Su Jok Academy.

Rubik, B. (2002). The biofield hypothesis: Its biophysical basis and role in medicine. Journal of Alternative and Complementary Medicine, 8(6), 703-717.

Korotkov, K. G., De Vito, D., Anufriev, K., et al. (2008). Electrophotonic analysis in medicine: GDV technique data. Journal of Applied Physics, 6, 1-18.