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EEG Metrics

구축 방법 — EEG Metrics · v1.0 · 2026-10-26

■ 방법

  • EEG 지표 14개를 같은 기록에 계산해 보여 주는 학습 주제. 탭 4개(지표 그룹): Arousal & Valence / Attention, Workload & Drowsiness / Spectral Shape / Connectivity & Events
  • Spectrum: Welch, 256점 Hann, 절반 겹침, 구간 평균 빼기(scipy.signal.welch 기본과 같음). Band(정한 값): delta 1–4, theta 4–8, alpha 8–13, beta 13–30, gamma 30–45 Hz
  • Filter: 4차 Butterworth high-pass·low-pass(2차 두 단), 60 Hz notch(Q 8), 앞뒤로 두 번 걸러 위상 지연 없음, 양 끝 1초 거울 반사
  • Reference: recorded / average / bipolar montage(double banana 18쌍) / neighbor bipolar / small Laplacian(이웃 4곳 평균 빼기)
  • Metrics: 1.6초(256점) window를 0.25초(40점)씩 밀며 계산(238개). Arousal β/α(F3+F4), Valence FAA ln α(R) − ln α(L), Engagement Index Σβ/(Σα+Σθ)(Cz·Pz·P3·P4), Theta/Beta Ratio(Cz), Mental Workload θ(Fz)/α(Pz), Drowsiness Index Σ(θ+α)/Σβ(Fz·Cz·Pz·Oz), Relative Alpha(Oz), IAF(O1·Oz·O2 7–14 Hz 최댓값), SEF95·Spectral Entropy(Fz 1–45 Hz), Alpha Coherence(Welch 256·절반 겹침, 8–13 Hz 평균). ERP·Sleep Staging은 설명만
  • 전처리: Python 3 · MNE 1.13.2 · NumPy 2.5.3 · SciPy 1.18.1 · wfdb. 파이썬 기준값은 브라우저와 같은 필터(python/jsfilt.py)를 씀. 실행 prepare 20261025T102831Z · measures 20261025T114536Z
  • 화면 계산: 직접 쓴 JavaScript(eeg-core.js, bci-core.js), 외부 라이브러리 없음
  • LLM 없음

■ 데이터

  • EEG Motor Movement/Imagery Dataset 1.0.0 (PhysioNet, Schalk 외) · ODC-By 1.0 · 64채널 160 Hz · S001 R02(눈 감은 휴식 61초)·R01(눈 뜬 휴식 61초) → EEG 기초·Metrics
  • 모든 원본 파일을 PhysioNet SHA256SUMS로 대조함
  • 브라우저용: 0.1 µV 단위 16비트 정수, base64(EEG 기초 R02·R01 각 1.67 MB, P300 1.05 MB, SSVEP 1.02 MB, Motor Imagery 1.35 MB). BCI 데이터는 BCI 탭을 열 때 받음
  • 성격: 공개 실측 데이터. EEGMMIDB의 기록 reference 위치는 데이터 설명에 없음
  • 사용자 입력 저장 없음

■ 검증

  • 브라우저 ↔ 파이썬 대조: band 상대 세기 64×5 최대 차이 0.000005 · Metrics 10개 × reference 2 × recording 2 중앙값 최대 상대 차이 0.0017 · Coherence 일치 · CCA 일치(0.650378) · P300·SSVEP·Motor Imagery 정확도와 ERD 곡선 일치
  • Metrics 중앙값(recorded reference, 눈 감음 → 눈 뜸): Arousal 0.514 → 0.915 · FAA −0.028 → −0.021 · Engagement 0.230 → 0.443 · TBR 1.10 → 1.45 · Mental Workload 0.273 → 1.57 · Drowsiness 4.22 → 2.20 · Relative Alpha 61.3% → 13.5% · IAF 10.0 → 9.4 Hz · SEF95 23.1 → 23.1 Hz · Spectral Entropy 0.697 → 0.662
  • Alpha Coherence Oz–Fz(눈 감음): recorded reference 0.046, average reference 0.701
  • signal.aileelab.com으로 옮긴 뒤 같은 검사(tests/eeg/core-test.mjs)를 다시 돌려 위 수치가 모두 그대로임을 확인(2026-10-26)

■ 범위

  • EEG 기초·Metrics는 건강한 자원자 1명의 쉬는 기록 2분. 지표 값은 정상 기준이나 감정 판정이 아님. BCI 체험은 각 데이터의 참가자 1–3명이며 다른 사람·장비의 성능을 대신하지 않음

■ 출처

  • Schalk, G. et al. (2004). BCI2000: A general-purpose brain-computer interface (BCI) system. IEEE Transactions on Biomedical Engineering 51(6).
  • Goldberger, A. et al. (2000). PhysioBank, PhysioToolkit, and PhysioNet. Circulation 101(23).
  • Russell, J.A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology 39(6).
  • Koelstra, S. et al. (2012). DEAP: A database for emotion analysis using physiological signals. IEEE Transactions on Affective Computing 3(1).
  • Davidson, R.J. (1992). Brain and Cognition 20(1); Coan, J.A., Allen, J.J.B. (2004). Biological Psychology 67(1–2); van der Vinne, N. et al. (2017). NeuroImage: Clinical 16.
  • Pope, A.T., Bogart, E.H., Bartolome, D.S. (1995). Biological Psychology 40(1–2).
  • Arns, M., Conners, C.K., Kraemer, H.C. (2013). Journal of Attention Disorders 17(5).
  • Gevins, A., Smith, M.E. (2003). Theoretical Issues in Ergonomics Science 4(1–2).
  • Jap, B.T. et al. (2009). Expert Systems with Applications 36(2).
  • Klimesch, W. (1999). Brain Research Reviews 29(2–3).
  • Rampil, I.J. (1998). Anesthesiology 89(4); Viertiö-Oja, H. et al. (2004). Acta Anaesthesiologica Scandinavica 48(2).
  • Nunez, P.L. et al. (1997). Electroencephalography and Clinical Neurophysiology 103(5).
  • Berry, R.B. et al. The AASM Manual for the Scoring of Sleep and Associated Events.
  • 데이터: EEG Motor Movement/Imagery Dataset 1.0.0 · ODC-By 1.0 · https://physionet.org/content/eegmmidb/1.0.0/
  • 도구: MNE-Python 1.13.2 · BSD-3 · wfdb · MIT (전처리 때만 사용)

■ 이력

  • 2026-10-26 · v1.0 · 공개

How it's built — EEG Metrics · v1.0 · 2026-10-26

■ Method

  • A learning topic that computes 14 EEG metrics on the same recording. 4 tabs (metric groups): Arousal & Valence / Attention, Workload & Drowsiness / Spectral Shape / Connectivity & Events
  • Spectrum: Welch, 256-point Hann, half overlap, segment mean removed (same as scipy.signal.welch defaults). Bands (set values): delta 1–4, theta 4–8, alpha 8–13, beta 13–30, gamma 30–45 Hz
  • Filters: 4th-order Butterworth high-pass and low-pass (two 2nd-order stages), 60 Hz notch (Q 8), forward and backward for zero phase, 1 s mirror padding
  • References: recorded / average / bipolar montage (18-pair double banana) / neighbor bipolar / small Laplacian (minus the mean of 4 neighbors)
  • Metrics: 1.6 s (256-point) windows moved by 0.25 s (40 points), 238 windows. Arousal β/α (F3+F4), Valence FAA ln α(R) − ln α(L), Engagement Index Σβ/(Σα+Σθ) (Cz, Pz, P3, P4), Theta/Beta Ratio (Cz), Mental Workload θ(Fz)/α(Pz), Drowsiness Index Σ(θ+α)/Σβ (Fz, Cz, Pz, Oz), Relative Alpha (Oz), IAF (peak 7–14 Hz over O1, Oz, O2), SEF95 and Spectral Entropy (Fz, 1–45 Hz), Alpha Coherence (Welch 256, half overlap, 8–13 Hz mean). ERP and Sleep Staging are described only
  • Preprocessing: Python 3 · MNE 1.13.2 · NumPy 2.5.3 · SciPy 1.18.1 · wfdb. Python references use the same filters as the browser (python/jsfilt.py). Runs: prepare 20261025T102831Z · measures 20261025T114536Z
  • On-page computation: hand-written JavaScript (eeg-core.js, bci-core.js), no external libraries
  • No LLM

■ Data

  • EEG Motor Movement/Imagery Dataset 1.0.0 (PhysioNet, Schalk et al.) · ODC-By 1.0 · 64 channels, 160 Hz · S001 R02 (eyes-closed rest, 61 s) and R01 (eyes-open rest, 61 s) → EEG basics and Metrics
  • Every source file was checked against PhysioNet's SHA256SUMS
  • Browser format: 16-bit integers in 0.1 µV units, base64 (basics R02 and R01 1.67 MB each, P300 1.05 MB, SSVEP 1.02 MB, Motor Imagery 1.35 MB). BCI data loads when the BCI tab opens
  • Nature: public measured data. The EEGMMIDB recording reference is not stated in the dataset description
  • Nothing typed by visitors is saved

■ Validation

  • Browser vs Python: relative band power 64×5 within 0.000005 · 10 metrics × 2 references × 2 recordings, medians within 0.0017 relative · coherence agrees · CCA agrees (0.650378) · P300, SSVEP and Motor Imagery accuracies and ERD curves agree
  • Metric medians (recorded reference, eyes closed → eyes open): Arousal 0.514 → 0.915 · FAA −0.028 → −0.021 · Engagement 0.230 → 0.443 · TBR 1.10 → 1.45 · Mental Workload 0.273 → 1.57 · Drowsiness 4.22 → 2.20 · Relative Alpha 61.3% → 13.5% · IAF 10.0 → 9.4 Hz · SEF95 23.1 → 23.1 Hz · Spectral Entropy 0.697 → 0.662
  • Alpha Coherence Oz–Fz (eyes closed): recorded reference 0.046, average reference 0.701
  • After the move to signal.aileelab.com the same checks (tests/eeg/core-test.mjs) were rerun and every number above was unchanged (2026-10-26)

■ Scope

  • EEG basics and Metrics use two minutes of rest from one healthy volunteer; metric values are not norms or emotion judgments. The BCI demos use 1–3 participants per dataset and do not stand for other people or devices

■ Sources

  • Schalk, G. et al. (2004). BCI2000: A general-purpose brain-computer interface (BCI) system. IEEE Transactions on Biomedical Engineering 51(6).
  • Goldberger, A. et al. (2000). PhysioBank, PhysioToolkit, and PhysioNet. Circulation 101(23).
  • Russell, J.A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology 39(6).
  • Koelstra, S. et al. (2012). DEAP: A database for emotion analysis using physiological signals. IEEE Transactions on Affective Computing 3(1).
  • Davidson, R.J. (1992). Brain and Cognition 20(1); Coan, J.A., Allen, J.J.B. (2004). Biological Psychology 67(1–2); van der Vinne, N. et al. (2017). NeuroImage: Clinical 16.
  • Pope, A.T., Bogart, E.H., Bartolome, D.S. (1995). Biological Psychology 40(1–2).
  • Arns, M., Conners, C.K., Kraemer, H.C. (2013). Journal of Attention Disorders 17(5).
  • Gevins, A., Smith, M.E. (2003). Theoretical Issues in Ergonomics Science 4(1–2).
  • Jap, B.T. et al. (2009). Expert Systems with Applications 36(2).
  • Klimesch, W. (1999). Brain Research Reviews 29(2–3).
  • Rampil, I.J. (1998). Anesthesiology 89(4); Viertiö-Oja, H. et al. (2004). Acta Anaesthesiologica Scandinavica 48(2).
  • Nunez, P.L. et al. (1997). Electroencephalography and Clinical Neurophysiology 103(5).
  • Berry, R.B. et al. The AASM Manual for the Scoring of Sleep and Associated Events.
  • Data: EEG Motor Movement/Imagery Dataset 1.0.0 · ODC-By 1.0 · https://physionet.org/content/eegmmidb/1.0.0/
  • Tools: MNE-Python 1.13.2 · BSD-3 · wfdb · MIT (preprocessing only)

■ History

  • 2026-10-26 · v1.0 · Released