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Back to the Future of qEEG: Lifespan Normative Modeling of Spectral Ratios and Functional Indices with Potential Applications to Therapeutic Monitoring.

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2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches
  1. [1] § Materials and Methods › Normative Modeling and Z-Score Derivation › GAMLSS Normative Framework ↔ code/fit_gamlss_v4.R, lines 91–177 · score 0.70 · JSU, SHASHo, concentration, slab, spike, zero
  2. [2] § Materials and Methods › Normative Modeling and Z-Score Derivation › GAMLSS Normative Framework ↔ code/fit_gamlss_v4.R, lines 91–177 · score 0.66 · Shapiro, kurt, excess, redundant, absorbing, BCPE

Paper

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The authors' code

R · 353 lines · 15 KB · no license · 2 matches

  1. library(gamlss)
  3. # ── Helpers de transformación ─────────────────────────────────────────────────
  4. apply_transform <- function(y, transform) {
  5. switch(transform,
  6. "none" = y,
  7. "log" = { if (any(y <= 0, na.rm=TRUE))
  8. stop(sprintf("transform='log' requiere y>0 (%d valores<=0)",
  9. sum(y <= 0, na.rm=TRUE)))
  10. log(y) },
  11. "log1p" = log(y + 1),
  12. "sqrt" = { if (any(y < 0, na.rm=TRUE))
  13. stop(sprintf("transform='sqrt' requiere y>=0 (%d valores<0)",
  14. sum(y < 0, na.rm=TRUE)))
  15. sqrt(y) },
  16. stop(sprintf("transform desconocido: '%s'", transform))
  17. )
  18. }
  20. apply_inv_transform <- function(y_t, transform) {
  21. switch(transform,
  22. "none" = y_t,
  23. "log" = exp(y_t),
  24. "log1p" = exp(y_t) - 1,
  25. "sqrt" = y_t^2,
  26. stop(sprintf("inv_transform desconocido: '%s'", transform))
  27. )
  28. }
  30. # ── Función principal ─────────────────────────────────────────────────────────
  31. fit_one_prefix <- function(prefix, DATA_DIR=".", N_AGE_GRID=500, FAMILY="NO") {
  33. input_file <- file.path(DATA_DIR, paste0(prefix, "_gamlss_data.csv"))
  34. if (!file.exists(input_file)) {
  35. cat(sprintf("[%s] archivo no encontrado, SKIP\n", prefix))
  36. return(invisible(NULL))
  37. }
  39. cat(sprintf("\n%s\nPREFIJO: %s\n%s\n", strrep("=",60), prefix, strrep("=",60)))
  41. # ── Leer transformación desde _gamlss_meta.csv (generado por export_for_gamlss.m) ──
  42. transform <- "none"
  43. meta_file <- file.path(DATA_DIR, paste0(prefix, "_gamlss_meta.csv"))
  44. if (file.exists(meta_file)) {
  45. meta <- read.csv(meta_file, stringsAsFactors=FALSE)
  46. meta_map <- setNames(meta$value, meta$field)
  47. if ("transform" %in% names(meta_map))
  48. transform <- meta_map[["transform"]]
  49. cat(sprintf(" transform = '%s' (leído de %s)\n", transform, basename(meta_file)))
  50. } else {
  51. cat(sprintf(" transform = 'none' (no se encontró %s)\n", basename(meta_file)))
  52. }
  54. dat <- read.csv(input_file, stringsAsFactors=FALSE, na.strings="NA")
  55. age <- dat$age
  56. var_names <- setdiff(names(dat), c("age","equipment"))
  57. N <- nrow(dat); P <- length(var_names)
  58. cat(sprintf(" %d sujetos, %d variables\n", N, P))
  60. age_valid <- age[is.finite(age) & age > 0]
  61. age_grid <- exp(seq(log(max(min(age_valid), 1)),
  62. log(max(age_valid)),
  63. length.out = N_AGE_GRID))
  65. results_centiles <- data.frame(age = age_grid)
  66. results_diagnostics <- data.frame(
  67. variable = var_names,
  68. pct_outside = NA_real_,
  69. n_valid = NA_integer_,
  70. family = NA_character_,
  71. transform_used = NA_character_,
  72. stringsAsFactors = FALSE
  73. )
  74. coef_export <- data.frame(age = age_grid)
  75. centile_levels <- c(0.025, 0.10, 0.25, 0.50, 0.75, 0.90, 0.975)
  76. centile_names <- c("p025","p10","p25","p50","p75","p90","p975")
  77. n_ok <- 0
  79. for (p in seq_along(var_names)) {
  80. vname <- var_names[p]
  81. y <- dat[[vname]]
  82. valid <- !is.na(y) & is.finite(y) & !is.na(age) & is.finite(age) & age > 0
  83. yy_orig <- y[valid]
  84. aa <- age[valid]
  85. n_v <- sum(valid)
  86. results_diagnostics$n_valid[p] <- n_v
  88. cat(sprintf(" Ajustando %s (%d/%d, n=%d)... ", vname, p, P, n_v))
  89. if (n_v < 30) { cat("SKIP (n<30)\n"); next }
  91. # ── Columna constante = 0: no hay nada que modelar ────────────────────
  92. # Ocurre con índices de asimetría cuando todos los sujetos tienen el mismo
  93. # valor (ej. electrodo faltante) o con variables que no aplican al prefijo.
  94. if (all(yy_orig == 0)) {
  95. cat("SKIP (todos cero)\n")
  96. results_diagnostics$family[p] <- "ZERO"
  97. results_diagnostics$transform_used[p] <- "none"
  98. results_diagnostics$pct_outside[p] <- 0
  99. # Rellenar curvas y centiles con ceros para que MATLAB pueda leer el CSV
  100. for (cn in centile_names) {
  101. results_centiles[[paste0(vname, "_", cn)]] <- rep(0, N_AGE_GRID)
  102. }
  103. results_centiles[[paste0(vname, "_mu")]] <- rep(0, N_AGE_GRID)
  104. results_centiles[[paste0(vname, "_sigma")]] <- rep(0, N_AGE_GRID)
  105. coef_export[[paste0(vname, "_mu")]] <- rep(0, N_AGE_GRID)
  106. coef_export[[paste0(vname, "_sigma")]] <- rep(0, N_AGE_GRID)
  107. next
  108. }
  110. # ── Selección de familia: evaluar SIEMPRE sobre datos CRUDOS (yy_orig) ──
  111. # Los criterios distribucionales se evalúan en escala original porque:
  112. # (1) BCT y BCPE requieren y > 0, que se cumple en datos crudos positivos
  113. # (2) Si los datos crudos son positivos y necesitan BCT, la transformación
  114. # log es redundante — BCT maneja asimetría y colas con su parámetro nu.
  115. # (3) Si los datos crudos no son positivos (ej. Valence), BCT puede usarse
  116. # directamente sobre ellos si n_crit >= 2.
  117. # La transformación log (u otra) solo se aplica si la familia elegida es NO.
  119. kurt_val <- mean((yy_orig - mean(yy_orig))^4) / var(yy_orig)^2
  120. kurt_excess <- kurt_val - 3
  121. skew_val <- mean((yy_orig - mean(yy_orig))^3) / var(yy_orig)^1.5
  122. sw_p <- tryCatch({
  123. n_sw <- min(length(yy_orig), 2000)
  124. set.seed(42)
  125. samp <- if (length(yy_orig) > n_sw) sample(yy_orig, n_sw) else yy_orig
  126. shapiro.test(samp)$p.value
  127. }, error = function(e) 1.0)
  129. crit_kurt <- kurt_excess > 1
  130. crit_skew <- abs(skew_val) > 0.5
  131. crit_sw <- sw_p < 0.05
  132. n_crit <- sum(c(crit_kurt, crit_skew, crit_sw))
  134. all_positive_orig <- all(yy_orig > 0)
  136. if (n_crit >= 2 && all_positive_orig) {
  137. # Datos positivos + no-Normal → BCT/BCPE sobre datos CRUDOS
  138. # (transform log es redundante; BCT lo maneja con nu)
  139. fam_candidates <- c("BCT", "BCPE", "NO")
  140. yy <- yy_orig # usar datos crudos, ignorar transform
  141. transform_used <- "none" # BCT absorbe la asimetría
  142. cat(sprintf("[kurt_ex=%.2f skew=%.2f SW_p=%.3f -> BCT(raw,%d/3)] ",
  143. kurt_excess, skew_val, sw_p, n_crit))
  144. } else if (n_crit >= 2 && !all_positive_orig) {
  145. # Datos con negativos + no-Normal:
  146. # Empíricamente verificado que para distribuciones spike-and-slab
  147. # (ej. Valence/FAA: masa concentrada en 0 con colas largas),
  148. # las familias JSU/SHASHo sobreestiman sigma y empeoran la
  149. # calibración de centiles centrales respecto a NO.
  150. # NO es la opción más robusta: el pct_outside global (~5%) es correcto
  151. # y los Z-scores extremos (|Z|>2) — el uso clínico principal — son válidos.
  152. fam_candidates <- c("NO")
  153. yy <- yy_orig
  154. transform_used <- "none"
  155. cat(sprintf("[kurt_ex=%.2f skew=%.2f SW_p=%.3f -> NO(neg,spike-slab,%d/3)] ",
  156. kurt_excess, skew_val, sw_p, n_crit))
  157. } else if (n_crit == 1 && all_positive_orig) {
  158. fam_candidates <- c("BCPE", "NO")
  159. yy <- yy_orig
  160. transform_used <- "none"
  161. cat(sprintf("[kurt_ex=%.2f skew=%.2f SW_p=%.3f -> BCPE(raw,%d/3)] ",
  162. kurt_excess, skew_val, sw_p, n_crit))
  163. } else {
  164. # Normal: aplicar transform si fue solicitado
  165. fam_candidates <- c("NO")
  166. transform_used <- transform
  167. yy <- tryCatch(
  168. apply_transform(yy_orig, transform),
  169. error = function(e) {
  170. cat(sprintf(" ERROR transform '%s': %s\n", transform, e$message))
  171. NULL
  172. }
  173. )
  174. if (is.null(yy)) next
  175. cat(sprintf("[kurt_ex=%.2f skew=%.2f SW_p=%.3f -> NO(transf='%s',%d/3)] ",
  176. kurt_excess, skew_val, sw_p, transform_used, n_crit))
  177. }
  179. # ── Nombres únicos en GlobalEnv ───────────────────────────────────────
  180. env_name_fam <- paste0(".gamlss_fam_", prefix, "_", p)
  181. env_name_df <- paste0(".gamlss_df_", prefix, "_", p)
  182. env_name_pr <- paste0(".gamlss_pr_", prefix, "_", p)
  184. df_tr <- data.frame(yy = yy, aa = aa)
  185. df_pr <- data.frame(aa = age_grid)
  187. fit <- NULL
  188. fam_used <- NA_character_
  190. for (fam_try in fam_candidates) {
  191. fam <- fam_try
  192. assign(env_name_fam, fam, envir = .GlobalEnv)
  193. assign(env_name_df, df_tr, envir = .GlobalEnv)
  194. assign(env_name_pr, df_pr, envir = .GlobalEnv)
  195. assign("yy", yy, envir = .GlobalEnv)
  196. assign("aa", aa, envir = .GlobalEnv)
  197. assign("fam", fam, envir = .GlobalEnv)
  199. fit_try <- tryCatch({
  200. fit_obj <- gamlss(
  201. yy ~ pb(log(aa)),
  202. sigma.formula = ~ pb(log(aa)),
  203. family = fam,
  204. data = df_tr,
  205. control = gamlss.control(n.cyc = 50, trace = FALSE)
  206. )
  207. # Parchar call para que predict() encuentre los datos
  208. fit_obj$call$data <- as.name(env_name_df)
  209. fit_obj$call$family <- as.name(env_name_fam)
  210. fit_obj$sigma.call$data <- as.name(env_name_df)
  211. fit_obj
  212. },
  213. error = function(e) {
  214. cat(sprintf("[%s->ERR:%s] ", fam_try,
  215. gsub("\n","",substr(e$message,1,40))))
  216. NULL
  217. })
  219. if (!is.null(fit_try)) {
  220. fit <- fit_try
  221. fam_used <- fam_try
  222. # Avisar si hubo fallback
  223. if (fam_try != fam_candidates[1]) {
  224. cat(sprintf("[fallback->%s] ", fam_try))
  225. }
  226. break
  227. }
  228. }
  230. if (is.null(fit)) { cat("FALLO TOTAL\n"); next }
  231. fam <- fam_used
  233. # ── Diagnóstico: residuos normalizados (ya en fit$residuals) ─────────
  234. pct_out <- mean(abs(fit$residuals) > 1.96, na.rm = TRUE) * 100
  235. results_diagnostics$pct_outside[p] <- round(pct_out, 2)
  236. results_diagnostics$family[p] <- fam
  237. results_diagnostics$transform_used[p] <- transform_used
  238. cat(sprintf("OK (%.1f%%)\n", pct_out))
  239. n_ok <- n_ok + 1
  241. # ── Predict mu y sigma sobre la rejilla de edad ───────────────────────
  242. mu_p <- tryCatch(
  243. predict(fit, newdata=df_pr, type="response", what="mu", data=df_tr),
  244. error = function(e) {
  245. cat(sprintf(" WARN predict mu: %s\n", e$message))
  246. rep(NA_real_, N_AGE_GRID)
  247. }
  248. )
  249. sig_p <- tryCatch(
  250. predict(fit, newdata=df_pr, type="response", what="sigma", data=df_tr),
  251. error = function(e) {
  252. cat(sprintf(" WARN predict sigma: %s\n", e$message))
  253. rep(NA_real_, N_AGE_GRID)
  254. }
  255. )
  257. # ── Predecir nu y tau fuera del loop (una sola vez por variable) ────────
  258. nu_p <- NULL
  259. tau_p <- NULL
  260. if (fam %in% c("BCT","BCPE")) {
  261. nu_p <- tryCatch(
  262. predict(fit, newdata=df_pr, type="response", what="nu", data=df_tr),
  263. error = function(e) rep(NA_real_, N_AGE_GRID)
  264. )
  265. }
  266. if (fam == "BCT") {
  267. tau_p <- tryCatch(
  268. predict(fit, newdata=df_pr, type="response", what="tau", data=df_tr),
  269. error = function(e) rep(NA_real_, N_AGE_GRID)
  270. )
  271. }
  273. # ── Centiles en espacio TRANSFORMADO, luego back-transform ────────────
  274. centile_vals_t <- list()
  275. for (ci in seq_along(centile_levels)) {
  276. cv_t <- if (fam == "NO") {
  277. mu_p + qnorm(centile_levels[ci]) * sig_p
  278. } else if (fam == "BCT") {
  279. tryCatch(
  280. qBCT(centile_levels[ci], mu=mu_p, sigma=sig_p, nu=nu_p, tau=tau_p),
  281. error = function(e) mu_p + qnorm(centile_levels[ci]) * sig_p
  282. )
  283. } else if (fam == "BCPE") {
  284. tryCatch(
  285. qBCPE(centile_levels[ci], mu=mu_p, sigma=sig_p, nu=nu_p),
  286. error = function(e) mu_p + qnorm(centile_levels[ci]) * sig_p
  287. )
  288. } else {
  289. mu_p + qnorm(centile_levels[ci]) * sig_p
  290. }
  291. centile_vals_t[[centile_names[ci]]] <- cv_t
  292. # Back-transform al espacio original antes de guardar
  293. results_centiles[[paste0(vname, "_", centile_names[ci])]] <-
  294. apply_inv_transform(cv_t, transform_used)
  295. }
  297. # mu y sigma se guardan en espacio TRANSFORMADO (necesario para Z-scores)
  298. results_centiles[[paste0(vname, "_mu")]] <- mu_p
  299. results_centiles[[paste0(vname, "_sigma")]] <- sig_p
  301. # sigma_eff basado en IQR 2.5–97.5 en espacio transformado
  302. sig_eff <- (centile_vals_t[["p975"]] - centile_vals_t[["p025"]]) / (2 * 1.96)
  303. coef_export[[paste0(vname, "_mu")]] <- mu_p
  304. coef_export[[paste0(vname, "_sigma")]] <- sig_eff
  305. if (!is.null(nu_p)) coef_export[[paste0(vname, "_nu")]] <- nu_p
  306. if (!is.null(tau_p)) coef_export[[paste0(vname, "_tau")]] <- tau_p
  309. # ── Limpiar GlobalEnv ─────────────────────────────────────────────────
  310. rm(list = intersect(c(env_name_fam, env_name_df, env_name_pr,
  311. "yy", "aa", "fam"),
  312. ls(envir = .GlobalEnv)),
  313. envir = .GlobalEnv)
  314. }
  316. # ── Guardar resultados ────────────────────────────────────────────────────
  317. write.csv(results_centiles, file.path(DATA_DIR, paste0(prefix, "_gamlss_centiles.csv")), row.names=FALSE)
  318. write.csv(results_diagnostics, file.path(DATA_DIR, paste0(prefix, "_gamlss_diagnostics.csv")), row.names=FALSE)
  319. write.csv(coef_export, file.path(DATA_DIR, paste0(prefix, "_gamlss_curves.csv")), row.names=FALSE)
  320. # Metadatos del modelo: transform aplicado + nombre del prefix
  321. write.csv(data.frame(field = c("transform","prefix"),
  322. value = c(transform, prefix),
  323. stringsAsFactors = FALSE),
  324. file.path(DATA_DIR, paste0(prefix, "_gamlss_model_meta.csv")),
  325. row.names=FALSE)
  327. pct_vals <- results_diagnostics$pct_outside[!is.na(results_diagnostics$pct_outside)]
  328. cat(sprintf("\n Ajustadas: %d/%d mediana: %.1f%%\n Curvas: %s\n",
  329. n_ok, P,
  330. ifelse(length(pct_vals) > 0, median(pct_vals), NA),
  331. paste0(prefix, "_gamlss_curves.csv")))
  333. # Limpieza final por si quedó algo
  334. stale <- ls(envir=.GlobalEnv, pattern=paste0("^\\.gamlss_.*_", prefix, "_"))
  335. if (length(stale) > 0) rm(list=stale, envir=.GlobalEnv)
  337. invisible(list(centiles = results_centiles,
  338. diagnostics = results_diagnostics,
  339. curves = coef_export))
  340. }
  342. # ── Ejecutar todos los prefijos ───────────────────────────────────────────────
  343. PREFIXES <- c("AIb", "AIe", "Arousal","Valence","CognAf",
  344. "TB1325R","TB1325RFC",
  345. "DB1325R","DB1325RFC",
  346. "DB1325RF3F4Asym","TB1325RF3F4Asym",
  347. "DB1325RF7F8Asym","TB1325RF7F8Asym",
  348. "DB1325RT3T4Asym","TB1325RT3T4Asym",
  349. "ABR","TBR","TAR","EI",
  350. "DB1325RT5T6Asym","TB1325RT5T6Asym",
  351. "AsymIdx","IAF")
  353. for (px in PREFIXES) fit_one_prefix(px)

fit_gamlss_v4.R at commit 1bb350a, no license · at the source

Overview

Authors: J Bosch-Bayard1,2, J Guerrero-Sauzameda2, R I Bosch-Bayard3,4, R Pérez-Elvira5,6, A Bosch-Castro2, J Sánchez-Rodríguez2, A Flores2, K Flores2, E Resendiz-Flores2, A Ferrando7, P Ferrando7, L Galán-García8, F Mushtaq9, P Valdes-Sosa10, G A Chiarenza11, R J Biscay12, L Morales-Chacón13
ORCID iDs: J Bosch-Bayard, L Morales-Chacón
13 affiliations
  1. Faculty of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
  2. Centro Integrador del Movimiento, Mente y Conducta (CIMMCO), Querétaro, México
  3. State University of Zanzibar, Zanzibar, Tanzania
  4. Universidad de Ciencias Médicas de La Habana, Havana, Cuba
  5. Department of Psychobiology, Faculty of Psychology, Pontifical University of Salamanca, Salamanca, Spain
  6. Neuropsychophysiology Laboratory, NEPSA Rehabilitación Neurológica, Salamanca, Spain
  7. Neuropulse, Neurocare Center, Encarnación, Paraguay
  8. Cuban Neuroscience Center, Havana, Cuba
  9. University of Leeds, Leeds, UK
  10. MOE Key Lab for Neuroinformation, School of Life Science and Technology, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
  11. Centro Internazionale Disturbi di Apprendimento, CIDAAI, Attenzione, Iperattività, Milano, Italy
  12. Centro de Investigaciones en Matemática (CIMAT), Guanajuato, Mexico
  13. Universidad Internacional de La Rioja, Logroño, Spain
Institutions: Carl von Ossietzky Universität Oldenburg (Germany); Universidad de Ciencias Médicas de la Habana (Cuba); State University of Zanzibar (Tanzania); Universidad Pontificia de Salamanca (Spain); Cuban Neuroscience Center (Cuba); University of Leeds (United Kingdom); University of Electronic Science and Technology of China (China); Mathematics Research Center (Mexico); Universidad Internacional De La Rioja (Spain)
Journal: Brain topography, volume 39, issue 5, article 87
Dates: received 6 April 2026; accepted 23 July 2026; published online 31 July 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1007/s10548-026-01240-4 · PMID 42536113 · PMCID PMC13427868 · OpenAlex W7171922662
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), computational (subfield)
Methods: Machine learning, Preprocessing, Spectral & time-frequency, Physiology & signal measures
Keywords: Quantitative EEG, Normative models, GAMLSS, Spectral ratios (theta/beta, arousal), Electrode-level spectral normalization, Therapeutic monitoring
MeSH: Brain*, Electroencephalography*, Adolescent, Adult, Aged, Aged, 80 and over, Aging, Child, Child, Preschool, Female, Humans, Male, Middle Aged, Young Adult (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Carl von Ossietzky Universität Oldenburg
Citations: not cited yet (Europe PMC); 55 references in the paper

Abstract

Quantitative EEG (qEEG) provides objective, millisecond-resolution measures of brain dynamics. Despite decades of methodological advances, clinically relevant derived indices—spectral power ratios, cognitive-emotional state markers, and physiological parameters—are typically reported as raw values without the normative context required for individualized clinical inference. To develop the first systematic age-dependent normative models for this family of derived qEEG indices using a multinational database, enabling probabilistic Z-score interpretation at the individual level with potential applications in objective therapeutic monitoring. Normative modeling was applied to the HarMNqEEG database (n = 1,564 neurologically healthy participants, ages 5–97, 9 countries, eyes-closed resting state). Electrode-level Spectral Normalization (ESN) removed inter-individual and inter-device amplitude variability while preserving the neurophysiological interpretability of each index. Age-dependent normative trajectories were estimated using Generalized Additive Models for Location, Scale, and Shape (GAMLSS) with P-splines on log(age), allowing conditional mean and variance to vary non-linearly across the lifespan. GAMLSS modeling revealed significant non-linear age-dependent trajectories for all indices. Slow-wave-dominated ratios showed steep decreases from childhood to early adulthood, consistent with cortical maturation; alpha-dominated indices increased during adolescence before stabilizing. ESN normalization yielded well-calibrated normative residuals across the full age range for all indices, except Valence. For the Valence index, a quantile regression model is provided as the recommended normative reference due to its spike-and-slab marginal distribution. These normative models provide a principled, age-adjusted probabilistic framework for individual-level qEEG interpretation, based on eyes closed resting state recordings, laying the methodological groundwork for future clinical validation in diagnostic and therapeutic monitoring applications. The ESN strategy requires no knowledge of recording equipment, ensuring broad applicability across clinical and research settings.

Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s10548-026-01240-4.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

CCC-members/HarMNqEEG

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: ad77ce35673889a69215ba686c5cfb9727b665f8, 3 March 2025
Languages: MATLAB (46)
Size: 54 files, 46 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: EEGLAB (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
48 files
At the source: github.com/CCC-members/HarMNqEEG

oldgandalf/Normatives-spectral-based-indexes-from-EEG-resting-state

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 1bb350a485b3fdee990d0c69a4ad2e9e71c944cd, 18 May 2026
Languages: R (1)
Size: 30 files, 1 script
Software Heritage: not archived
Found in: “Data Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2 files
At the source: github.com/oldgandalf/Normatives-spectral-based-indexes-from-EEG-resting-state

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 47 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data Availability

The HarMNqEEG normative database is publicly available at https://github.com/CCC-members/HarMNqEEG. GAMLSS model parameters, centile curves, and Z-score computation scripts (MATLAB/R) will be released at https://github.com/oldgandalf/Normatives-spectral-based-indexes-from-EEG-resting-state upon acceptance, subject to a reasonable request to the corresponding author.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Publisher: n/a → Springer Science+Business Media
  • Funding: added Carl von Ossietzky Universität Oldenburg

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 17 authors, 7 keywords, 14 MeSH terms, 48 references.

Cite

This paper

Bosch-Bayard, J., Guerrero-Sauzameda, J., Bosch-Bayard, R. I., Pérez-Elvira, R., Bosch-Castro, A., Sánchez-Rodríguez, J., Flores, A., Flores, K., Resendiz-Flores, E., Ferrando, A., Ferrando, P., Galán-García, L., Mushtaq, F., Valdes-Sosa, P., Chiarenza, G. A., Biscay, R. J., & Morales-Chacón, L. (2026). Back to the Future of qEEG: Lifespan Normative Modeling of Spectral Ratios and Functional Indices with Potential Applications to Therapeutic Monitoring. Brain topography, 39(5), 87. https://doi.org/10.1007/s10548-026-01240-4

BibTeX

@article{boschbayard2026back,
author = {Bosch-Bayard, J and Guerrero-Sauzameda, J and Bosch-Bayard, R I and Pérez-Elvira, R and Bosch-Castro, A and Sánchez-Rodríguez, J and Flores, A and Flores, K and Resendiz-Flores, E and Ferrando, A and Ferrando, P and Galán-García, L and Mushtaq, F and Valdes-Sosa, P and Chiarenza, G A and Biscay, R J and Morales-Chacón, L},
title = {{Back to the Future of qEEG: Lifespan Normative Modeling of Spectral Ratios and Functional Indices with Potential Applications to Therapeutic Monitoring}},
journal = {Brain topography},
year = {2026},
month = jul,
volume = {39},
number = {5},
pages = {87},
publisher = {Springer Science+Business Media},
issn = {0896-0267},
doi = {10.1007/s10548-026-01240-4},
url = {https://doi.org/10.1007/s10548-026-01240-4},
pmid = {42536113},
pmcid = {PMC13427868}
}

RIS

TY - JOUR
AU - Bosch-Bayard, J
AU - Guerrero-Sauzameda, J
AU - Bosch-Bayard, R I
AU - Pérez-Elvira, R
AU - Bosch-Castro, A
AU - Sánchez-Rodríguez, J
AU - Flores, A
AU - Flores, K
AU - Resendiz-Flores, E
AU - Ferrando, A
AU - Ferrando, P
AU - Galán-García, L
AU - Mushtaq, F
AU - Valdes-Sosa, P
AU - Chiarenza, G A
AU - Biscay, R J
AU - Morales-Chacón, L
TI - Back to the Future of qEEG: Lifespan Normative Modeling of Spectral Ratios and Functional Indices with Potential Applications to Therapeutic Monitoring
T2 - Brain topography
J2 - Brain Topogr
PY - 2026
DA - 2026/07/31
VL - 39
IS - 5
SP - 87
SN - 0896-0267
PB - Springer Science+Business Media
DO - 10.1007/s10548-026-01240-4
UR - https://doi.org/10.1007/s10548-026-01240-4
LA - en
ER -

CSL-JSON

{
"id": "10.1007/s10548-026-01240-4",
"type": "article-journal",
"title": "Back to the Future of qEEG: Lifespan Normative Modeling of Spectral Ratios and Functional Indices with Potential Applications to Therapeutic Monitoring",
"container-title": "Brain topography",
"author": [
{
"family": "Bosch-Bayard",
"given": "J"
},
{
"family": "Guerrero-Sauzameda",
"given": "J"
},
{
"family": "Bosch-Bayard",
"given": "R I"
},
{
"family": "Pérez-Elvira",
"given": "R"
},
{
"family": "Bosch-Castro",
"given": "A"
},
{
"family": "Sánchez-Rodríguez",
"given": "J"
},
{
"family": "Flores",
"given": "A"
},
{
"family": "Flores",
"given": "K"
},
{
"family": "Resendiz-Flores",
"given": "E"
},
{
"family": "Ferrando",
"given": "A"
},
{
"family": "Ferrando",
"given": "P"
},
{
"family": "Galán-García",
"given": "L"
},
{
"family": "Mushtaq",
"given": "F"
},
{
"family": "Valdes-Sosa",
"given": "P"
},
{
"family": "Chiarenza",
"given": "G A"
},
{
"family": "Biscay",
"given": "R J"
},
{
"family": "Morales-Chacón",
"given": "L"
}
],
"container-title-short": "Brain Topogr",
"volume": "39",
"issue": "5",
"page": "87",
"DOI": "10.1007/s10548-026-01240-4",
"PMID": "42536113",
"PMCID": "PMC13427868",
"ISSN": "0896-0267",
"publisher": "Springer Science+Business Media",
"URL": "https://doi.org/10.1007/s10548-026-01240-4",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
31
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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