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neuroskill-data-reference

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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安装命令

npx skills add https://github.com/NeuroSkill-com/neuroloop-py --skill neuroskill-data-reference

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NeuroSkill-com/neuroloop-py

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更新时间2026年3月4日 18:31

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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.

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neuroskill-sleep

NeuroSkill-com/neuroloop-py

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.

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来源

NeuroSkill-com

NeuroSkill-com/neuroloop-py

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数据科学家计算机与数学类职业15-2051L4

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.

NeuroSkill Data Reference

EXG Band Powers

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.

EXG Ratios & Indices

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.

Core Scores

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.

Complexity Measures

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.

PPG / Heart Rate Variability

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.

Motion & Artifacts

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.

Sleep Stages

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

Consciousness Metrics

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.

References

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