The science

A window into the brain.

How eLova reads a recording, what each reading measures, and how to read a brain age.

4 short chapters 9 peer-reviewed references

01 The signal

The brain speaks in electricity.

Neurons communicate in small electrical signals. Sensors resting on the scalp can record their rhythm — nothing enters the body, and nothing is sent into it.

This is electroencephalography, or EEG, first recorded in a human in 1924 and in clinical use ever since.1 eLova reads any EEG recording — from a clinical system, a headset at a spa, or a headband at home — taken during a few quiet minutes at rest, eyes open and then closed. Where a heart-rhythm (ECG) channel is recorded, it contributes to the Stress reading.

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  • Any EEG deviceThe recording is what we read, not the hardware.
  • Resting stateEyes open, then eyes closed, at rest.
  • Standard positionsSensors in the international 10–20 layout, shown here.
  • Quality firstSignal quality is checked before any reading is made.

02 What we measure

Four readings. One index.

Each score runs from 0 to 100, measured against an age- and sex-matched reference population.

eLova Index

Composite · 0–100

The headline number: the four readings below, brought together.

Brain age

In years

The brain’s apparent age, estimated from resting-state EEG.

Focus

0–100

Attentional regulation, from established EEG markers of attention.2,3

Stress

0–100

Cortical arousal and heart-rate variability, read together.4–7 A higher score means a quicker return to calm.

Cognition

0–100

Neural processing speed and signal efficiency, from the brain’s alpha rhythm.8,9

03 Brain age

Reading the gap.

A model trained on more than 100,000 recordings estimates the age a brain most resembles. The gap between that number and a person’s actual age is the quiet signal.

A single reading is a landmark, not a verdict — the pattern across tests is what matters.

GapWhat it suggestsHow we frame it
−5 to +5Consistent with actual ageNeutral — the most common result
±5 to ±10Within the variation of a single readingNo conclusion yet; future readings will tell
Above +10A meaningful older-appearing patternModifiable factors; confirm over tests
Below −10A meaningful younger-appearing patternResilience, with a single-reading caveat
Beyond ±15Outside the expected range — usually technicalRe-record when rested; no interpretation

Under 30, the brain is still maturing, so readings are shown as a baseline rather than interpreted.

04 The boundary

What eLova is — and is not.

eLova is for general wellness. It describes what we see in the data — never what it means medically.

For general wellness use only
Not a medical device
Not intended to diagnose, treat, cure, mitigate or prevent any disease or condition
Does not constitute medical advice
Not a substitute for professional medical examination, diagnosis or treatment
If you have a health concern, please consult a qualified medical professional

Appendix

References.

  1. Schomer, D. L. (ed.) (2011). Niedermeyer’s Electroencephalography: Basic Principles, Clinical Applications, and Related Fields (6th ed.). Lippincott Williams & Wilkins.
  2. Putman, P., van Peer, J., Maimari, I., & van der Werff, S. (2010). EEG theta/beta ratio in relation to fear-modulated response-inhibition, attentional control, and affective traits. Biological Psychology, 83(2), 73–78. doi.org/10.1016/j.biopsycho.2009.10.008
  3. Loo, S. K., Hopfer, C., Teale, P. D., & Reite, M. L. (2004). EEG correlates of methylphenidate response in ADHD: association with cognitive and behavioral measures. Journal of Clinical Neurophysiology, 21(6), 457–464. doi.org/10.1097/01.wnp.0000150890.14421.9a
  4. Thayer, J. F., & Lane, R. D. (2000). A model of neurovisceral integration in emotion regulation and dysregulation. Journal of Affective Disorders, 61(3), 201–216. doi.org/10.1016/S0165-0327(00)00338-4
  5. Thayer, J. F., & Lane, R. D. (2009). Claude Bernard and the heart–brain connection: Further elaboration of a model of neurovisceral integration. Neuroscience & Biobehavioral Reviews, 33(2), 81–88. doi.org/10.1016/j.neubiorev.2008.08.004
  6. Nunan, D., Sandercock, G. R. H., & Brodie, D. A. (2010). A quantitative systematic review of normal values for short-term heart rate variability in healthy adults. Pacing and Clinical Electrophysiology, 33(11), 1407–1417. doi.org/10.1111/j.1540-8159.2010.02841.x
  7. Laborde, S., Mosley, E., & Thayer, J. F. (2017). Heart rate variability and cardiac vagal tone in psychophysiological research — Recommendations for experiment planning, data analysis, and data reporting. Frontiers in Psychology, 8, 213. doi.org/10.3389/fpsyg.2017.00213
  8. Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Research Reviews, 29(2–3), 169–195. doi.org/10.1016/S0165-0173(98)00056-3
  9. Klimesch, W., Sauseng, P., & Hanslmayr, S. (2007). EEG alpha oscillations: The inhibition–timing hypothesis. Brain Research Reviews, 53(1), 63–88. doi.org/10.1016/j.brainresrev.2006.06.003

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