fNIRS + Machine Learning: New Pathways for Living Diagnostics
fNIRS + Machine Learning: New Pathways for Living Diagnostics
First-Person Consciousness — Brain Bee Style
I always thought a diagnosis was something static:
numbers, tables, frozen images —
a result that appears only after the machine “finishes the calculation.”
But this week, while studying how fNIRS combined with Machine Learning is being used in real environments, I noticed something different:
the diagnosis is becoming alive.
It is no longer a photograph —
it is becoming a movement.
And that changes everything.
1. A living diagnosis is when the whole body becomes data — and the data becomes a story
New ML models applied to fNIRS do more than classify signals.
They recognize patterns of the body:
respiratory variation,
cognitive effort,
micro-conflicts of attention,
tensional collapse,
anticipation (Apus),
Zone 2 emerging,
Zone 3 trying to dominate,
reorganization of the Tensional Self,
fluctuations of consciousness during the task.
The diagnosis stops being a “result.”
It becomes a biological narrative,
almost as if the body were telling its own story in real time.
2. Machine Learning understands what the human eye could never see
The newest models detect:
subtle prefrontal alterations,
hybrid patterns across channels,
physiological distress signatures before symptoms,
the body attempting to compensate for a failure,
reorganizations of hemodynamic connectivity,
breathing trying to rescue Zone 2,
attentional overload typical of Zone 3.
And this is the most fascinating part:
ML doesn’t just see the signal — it identifies the state.
As if saying:
“Your body is trying to do X, but it is stuck in Y.”
It is diagnosis returning to belonging.
3. When ML meets the Damasian Mind, new biomarkers emerge
Recent algorithms confirm what we had already perceived:
states of fruição have stable hemodynamic signatures,
difficult decisions show micro-imbalances in oxygenation,
bodily prediction (Apus) contains information before movement,
Zone 2 has its own rhythm,
Zone 3 has typical turbulence,
the Tensional Self is identifiable as a physiological pattern.
Before, this was neuro-affective philosophy.
Now it is automatic classification.
4. Machine Learning can “predict” when the body is about to enter cognitive collapse
Models applied to fNIRS detect transitions that previously went unnoticed:
the moment the body is about to lose focus,
the onset of cognitive fatigue before the person senses it,
micro-threats that push the subject down to Zone 3,
the exact instant attention breaks,
the moment breathing loses coherence.
It is as if the system said:
“Your body is trying to warn you — I will warn you for it.”
This is the future of care.
5. Diagnosis without pathologizing — a living analysis of existential metabolism
The most revolutionary part is that fNIRS + ML does not search for disease.
It searches for pattern.
For state.
For trajectory.
It no longer asks:
X “What is wrong in the brain?”
But instead:
V “How is this body trying to live right now?”
Diagnosis ceases to be judgment
and becomes comprehension.
This is decolonial neuroscience applied to clinical practice.
6. Machine Learning validates our concepts with impressive clarity
Findings show that:
Apus is detectable with hemodynamic precedence,
Tensional Selves form physiological clusters,
Zone 2 appears as respiratory–cortical coherence,
Zone 3 emerges as hemodynamic noise,
Human Quorum Sensing (QSH) appears in inter-individual patterns,
the Damasian Mind emerges from hemodynamic interaction between body and environment.
fNIRS sees.
ML interprets.
Together, they translate the body.
7. First-person conclusion — The future of diagnosis is to feel together
After studying all this, I understood:
the diagnosis of the future does not analyze the brain.
It listens to the body.
It accompanies life.
It follows movement.
It is not a result.
It is a conversation between organism and technology.
And the most beautiful part:
When fNIRS meets ML, science stops looking at disease
and begins to look at ways of existing.
Diagnosis stops being a sentence
and becomes a living understanding of the person.
This blog is based on recent research (2020–2024)
in portable fNIRS, Machine Learning applied to functional neuroimaging, hemodynamic pattern identification, naturalistic neuroscience, biomarkers of cognitive effort, attentional states, respiratory neurophysiology, predictive analysis, and hybrid classifiers:
ML significantly improves fNIRS sensitivity and specificity;
hemodynamic signatures reflect states equivalent to Zones 1, 2, and 3;
anticipatory physiological patterns are consistent with the Apus concept;
the body generates Tensional Selves detectable by supervised and hybrid models;
group processes create signatures coherent with QSH;
fNIRS + ML transforms diagnoses into dynamic trajectories, not rigid categories.
fNIRS + Machine Learning: Nuevos Caminos para Diagnósticos Vivos
fNIRS + Machine Learning: New Pathways for Living Diagnostics
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