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NeuroSkill EXG data reference — all metric fields including band powers, EXG ratios and indices, core scores, complexity measures, PPG/HRV fields, motion and artifact markers, sleep stage codes, EXG correlate indices, and consciousness metrics. Use when looking up what a specific metric means or its value range.
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| name | neuroskill-data-reference |
| description | NeuroSkill EXG data reference — all metric fields including band powers, EXG ratios and indices, core scores, complexity measures, PPG/HRV fields, motion and artifact markers, sleep stage codes, EXG correlate indices, and consciousness metrics. Use when looking up what a specific metric means or its value range. |
Relative power — values sum to approximately 1.0.
Found under scores.bands in status, or as rel_* top-level keys in metric responses.
| Field | Band | Range | What it means |
|---|---|---|---|
rel_delta | δ 0.5–4 Hz | 0–1 | Deep sleep, unconscious processes. High during N3 sleep or drowsiness. |
rel_theta | θ 4–8 Hz | 0–1 | Drowsiness, meditation, creativity, memory encoding. High in ADHD (TBR). |
rel_alpha | α 8–13 Hz | 0–1 | Relaxed wakefulness, idle cortex, eyes-closed state. Drops on task engagement. |
rel_beta | β 13–30 Hz | 0–1 | Active thinking, focus, anxiety. High beta = cognitive effort or stress. |
rel_gamma | γ 30–100 Hz | 0–1 | Sensory binding, high-level cognition. Low in schizophrenia. |
| Field | Formula | What it means |
|---|---|---|
faa | ln(αR) − ln(αL) | Frontal Alpha Asymmetry. Positive = approach motivation / positive affect. Negative = withdrawal / depression. |
tar | θ / α | Theta/Alpha Ratio. High = drowsy or meditative. |
bar | β / α | Beta/Alpha Ratio. High = alert, possibly anxious. |
dtr | δ / θ | Delta/Theta Ratio. High in deep sleep or pathological slowing. |
tbr | θ / β | Theta/Beta Ratio. Main ADHD biomarker. Healthy ~1.0; elevated ADHD >1.5. |
pse | (power law slope) | Power Spectral Exponent. Steeper = more 1/f, typical of rest. Flatter = active. |
bps | (regression slope) | Band-Power Slope. Similar to PSE; measures spectral tilt. |
apf | Hz | Alpha Peak Frequency. 8–12 Hz typical; shifts with age and cognitive state. |
sef95 | Hz | Spectral Edge Frequency 95%. Frequency below which 95% of power falls. |
spectral_centroid | Hz | Spectral Centroid. Weighted average frequency — rises with cognitive load. |
coherence | 0–1 | Inter-channel coherence. High = coordinated brain activity. |
mu_suppression | 0–1 | Mu rhythm suppression. Increases with motor imagery or observed action. |
laterality_index | −1 to 1 | Hemispheric laterality. Left vs. right hemispheric dominance. |
snr | dB | Signal-to-Noise Ratio. > 10 dB = good signal; < 5 dB = noisy. |
0–1 range unless noted. Computed per 5-second epoch by the on-device model.
| Field | What it means |
|---|---|
focus | Sustained attention. Driven by frontal beta and suppressed alpha. |
relaxation | Calm, low-arousal state. High alpha, low beta. |
engagement | Active cognitive engagement. Composite of beta, theta, alpha suppression. |
meditation | Meditative depth. High frontal alpha, stable theta, low beta. |
mood | Valence estimate. Positive FAA and alpha balance → positive mood. |
cognitive_load | Mental effort. High theta + beta, low alpha. |
drowsiness | Sleepiness. High delta + theta, alpha intrusions. |
Nonlinear EXG measures — higher complexity generally means a more flexible, awake brain state.
| Field | What it means |
|---|---|
hjorth_activity | Signal variance (power). |
hjorth_mobility | Mean frequency estimate. |
hjorth_complexity | Signal shape complexity — how much the signal changes its frequency. |
permutation_entropy | Ordinal pattern entropy. Near 1 = complex/random; near 0 = highly ordered. |
higuchi_fd | Fractal dimension. ~1.5–1.8 during healthy wakefulness. |
dfa_exponent | Detrended fluctuation. ~0.5 = white noise; ~1.0 = long-range correlations. |
sample_entropy | Regularity — lower = more predictable/periodic signal. |
pac_theta_gamma | Phase-Amplitude Coupling (θ–γ). Linked to working memory and attention. |
Derived from the Muse PPG sensor (forehead).
| Field | Unit | What it means |
|---|---|---|
hr | bpm | Heart rate. |
rmssd | ms | Root mean square of successive differences — parasympathetic HRV. High = relaxed. |
sdnn | ms | Standard deviation of NN intervals — overall HRV. |
pnn50 | % | % of successive differences > 50 ms — parasympathetic index. |
lf_hf_ratio | ratio | Low/High frequency power ratio — sympathetic vs. parasympathetic balance. High = stress. |
respiratory_rate | bpm | Estimated breathing rate from PPG. |
spo2_estimate | % | Estimated blood oxygen saturation (research only). |
perfusion_index | % | Ratio of pulsatile to static IR signal — peripheral perfusion quality. |
stress_index | 0–100 | Composite stress index. High HR + low HRV + high LF/HF → high stress. |
| Field | What it means |
|---|---|
stillness | 0–1. Head movement score; 1 = no motion. |
head_pitch | Degrees forward/backward tilt. |
head_roll | Degrees left/right tilt. |
nod_count | Number of detected vertical head nods. |
shake_count | Number of detected horizontal head shakes. |
blink_count | Number of detected eye blinks (from frontal electrodes). |
blink_rate | Blinks per minute. |
jaw_clench_count | Number of detected jaw clenches (EMG artifact). |
jaw_clench_rate | Jaw clenches per minute. |
Used in sleep and status.sleep.
| Stage | Code | EXG signature |
|---|---|---|
| Wake | 0 | High beta, present alpha when eyes closed |
| N1 | 1 | Slow eye movements, alpha fades, theta begins |
| N2 | 2 | Sleep spindles (12–15 Hz bursts), K-complexes, dominant theta |
| N3 | 3 | High-amplitude delta > 50% of epoch — deep/slow-wave sleep |
| REM | 4 | Low-amplitude mixed frequency, sawtooth waves, suppressed delta |
From consciousness. All 0–100 (higher = better).
| Metric | What it measures | Reference (verified DOI) |
|---|---|---|
lzc | Lempel-Ziv Complexity proxy — signal diversity; drops under anesthesia | Casali et al. (2013) · doi:10.1126/scitranslmed.3006294 |
wakefulness | Inverse drowsiness — high alpha relative to theta | Klimesch (1999) · doi:10.1016/s0165-0173(98)00056-3 |
integration | Composite of coherence × PAC × spectral entropy — cortical integration | Tononi (2004) · doi:10.1186/1471-2202-5-42 |
Consciousness: ≥ 50 = green, 25–50 = yellow, < 25 = red.
All DOIs below are verified against the Skill application reference list (HelpReferences.svelte).
Only papers present in that list are cited here.
| # | Citation | DOI |
|---|---|---|
| [64] | Monastra, V. J., Lubar, J. F., Linden, M. (2001). The development of a quantitative electroencephalographic scanning process for ADHD. Neuropsychology, 15(1), 136–144. | doi:10.1037/0894-4105.15.1.136 |
| [66] | Demerdzieva, A., Pop-Jordanova, N. (2015). Relation Between Frontal Alpha Asymmetry and Anxiety in Young Patients with Generalized Anxiety Disorder. PRILOZI, 36(2), 157–177. | doi:10.1515/prilozi-2015-0064 |
| [67] | Stewart, J. L., Coan, J. A., Towers, D. A., Allen, J. J. B. (2014). Resting and task-elicited prefrontal EXG alpha asymmetry in depression. Psychophysiology, 51(5), 446–455. | doi:10.1111/psyp.12191 |
| [68] | Perlis, M. L., Merica, H., Smith, M. T., Giles, D. E. (2001). Beta EXG activity and insomnia. Sleep Medicine Reviews, 5(5), 365–376. | doi:10.1053/smrv.2001.0151 |
| [69] | Bjørk, M. H., Stovner, L. J., Engstrøm, M. et al. (2009). Interictal quantitative EXG in migraine: a blinded controlled study. The Journal of Headache and Pain, 10(5), 331–339. | doi:10.1007/s10194-009-0140-4 |
| [70] | Gloss, D. S., Nolan, S. J., Staba, R. (2014). The role of high-frequency oscillations in epilepsy surgery planning. Cochrane Database of Systematic Reviews. | doi:10.1002/14651858.cd010235.pub2 |
| [71] | Dauvilliers, Y. (2007). Narcolepsy with Cataplexy: Hypocretin and Immunological Aspects. In: Neuroimmunology of Sleep (Springer), pp. 337–352. | doi:10.1007/978-0-387-69146-6_20 |
| [72] | Jeong, J. (2004). EXG dynamics in patients with Alzheimer's disease. Clinical Neurophysiology, 115(7), 1490–1505. | doi:10.1016/j.clinph.2004.01.001 |
| [73] | Spironelli, C., Fusina, F., Bortolomasi, M., Angrilli, A. (2021). EXG Frontal Asymmetry in Dysthymia, Major Depressive Disorder and Euthymic Bipolar Disorder. Symmetry, 13(12), 2414. | doi:10.3390/sym13122414 |
| [74] | Uhlhaas, P. J., Singer, W. (2010). Abnormal neural oscillations and synchrony in schizophrenia. Nature Reviews Neuroscience, 11(2), 100–113. | doi:10.1038/nrn2774 |
| [75] | Casali, A. G., Gosseries, O., Rosanova, M. et al. (2013). A Theoretically Based Index of Consciousness Independent of Sensory Processing and Behavior. Science Translational Medicine, 5(198), 198ra105. | doi:10.1126/scitranslmed.3006294 |
| [6] | Klimesch, W. (1999). EXG alpha and theta oscillations reflect cognitive and memory performance. Brain Research Reviews, 29(2–3), 169–195. | doi:10.1016/s0165-0173(98)00056-3 |
| [76] | Tononi, G. (2004). An information integration theory of consciousness. BMC Neuroscience, 5, 42. | doi:10.1186/1471-2202-5-42 |
NeuroSkill `label`, `search-labels`, and `interactive` commands — creating EXG text annotations, semantic vector search over labels, and cross-modal 4-layer graph search combining text and EXG similarity. Use when annotating EXG moments or searching for past states by description.
Full guided-protocol repertoire for neuroloop — 70+ mind-body practices organised by EXG metric signal. Covers attention, stress, emotions, relaxation, sleep, somatic, consciousness, cognitive performance, motor, neck, eyes, morning routines, workout/gym, hydration, bathroom breaks, emotions, music, social-media addiction, and dietary protocols. Loaded on-demand when protocol context is detected.
NeuroSkill use-case recipes and scripting patterns — shell snippets for focus and productivity monitoring, stress and anxiety tracking, sleep quality analysis, ADHD/cognitive-load queries, meditation tracking, cross-modal graph search, A/B session comparison, time-range queries, and automation with cron/Python/Node.js/HTTP. Use when looking for practical examples or building automation pipelines.
NeuroSkill `search` and `compare` commands — ANN search for neurally similar EXG moments across all history, and A/B session comparison with metric deltas, trend directions, and UMAP enqueuing. Use when finding similar past brain states or comparing two recording sessions.
NeuroSkill `session` and `sessions` commands — per-session metric breakdowns with first/second-half trends, session listing across all days, and Unix timestamp helpers. Use when analysing a specific recording session or listing all available sessions.
NeuroSkill `sleep` and `umap` commands — EXG-based sleep stage classification (Wake/N1/N2/N3/REM) with efficiency and bout analysis, and 3D UMAP projection of session embeddings for spatial comparison. Use when analysing sleep quality or visualising neural state separation between sessions.
