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arxiv: 2503.19924 · v4 · submitted 2025-03-12 · 🧬 q-bio.NC · nlin.AO

Human brain state classification via permutation entropy of EEG phase dynamics across consciousness levels and inattentive-type ADHD

Athokpam Langlen Chanu , Youngjai Park , Jaesung Choi , Younghwa Cha , UnCheol Lee , Joon-Young Moon , Jong-Min Park This is my paper

Pith reviewed 2026-05-23 00:33 UTC · model grok-4.3

classification 🧬 q-bio.NC nlin.AO
keywords permutation entropyEEGphase dynamicsbrain statesconsciousnessADHDgeneral anesthesiaordinal patterns
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The pith

Permutation entropy of EEG phase dynamics distinguishes conscious from unconscious states but not ADHD groups.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines if permutation entropy of the principal mode of EEG phase dynamics can classify human brain states across consciousness levels and in inattentive-type ADHD. It applies this to anesthesia recordings and resting-state EEG from controls and ADHD subjects under eyes-open and eyes-closed conditions. The entropy shows dependence on brain state, with successful classification for consciousness distinctions and eye conditions, but poor separability for ADHD versus controls. This suggests ordinal patterns in phase dynamics suffice for consciousness but may miss key information for ADHD detection.

Core claim

Permutation entropy distributions from the principal mode of EEG phase dynamics exhibit a clear dependence on brain state. Conscious, inattentive-type ADHD, and eyes-closed conditions show lower mean values and larger standard deviations. Classification models using this entropy as input reliably capture conscious versus unconscious states in the anesthesia dataset and eyes-open versus eyes-closed in the resting-state dataset, but do not show clear separability between control and inattentive-type ADHD groups, indicating that original time-series values may be more crucial for ADHD detection.

What carries the argument

Permutation entropy applied to ordinal patterns of the principal mode of EEG phase dynamics reflecting anterior-posterior information flow.

If this is right

  • Conscious and unconscious states are reliably distinguishable in general anesthesia EEG data.
  • Eyes-open and eyes-closed conditions are distinguishable in resting-state EEG.
  • Control and inattentive-type ADHD groups show no clear separability using this measure.
  • Conscious, ADHD, and eyes-closed conditions have lower mean permutation entropy with larger standard deviations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The finding that amplitude information may be needed for ADHD suggests combining ordinal and amplitude features could improve detection.
  • This approach might apply to monitoring other altered states of consciousness beyond anesthesia.
  • Limitations in ADHD classification point to the need for multi-feature models in clinical diagnostics.

Load-bearing premise

Restricting analysis to the principal mode of EEG phase dynamics and applying permutation entropy to its ordinal patterns is sufficient to capture the disorder relevant to brain-state discrimination without the original time-series amplitude values.

What would settle it

Testing whether classification accuracy for ADHD improves when including the original EEG time-series amplitude values alongside the permutation entropy.

Figures

Figures reproduced from arXiv: 2503.19924 by Athokpam Langlen Chanu, Jaesung Choi, Jong-Min Park, Joon-Young Moon, UnCheol Lee, Younghwa Cha, Youngjai Park.

Figure 1
Figure 1. Figure 1: FIG. 1 [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (b) displays violin plots of permutation entropy (H) calculated from the entire β1(t) time series of each in￾dividual across different brain states in the general anes￾thesia dataset I (using Eq. (4) with dx = 6). Dashed lines connect H-values for four subjects exhibiting all seven brain states. The embedded boxplots show the quar￾tile distribution: the box spans the interquartile range (25th to 75th perce… view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3 [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4 [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5 [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIG. 6 [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: FIG. 7 [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
read the original abstract

We analyze electroencephalography (EEG) signals using the ordinal pattern framework to investigate whether different human brain states can be distinguished based on the disorder of EEG dynamics. Rather than analyzing raw EEG signals, we focus on the principal mode of EEG phase dynamics, reflecting anterior-posterior information flow, and quantify disorder using permutation entropy. We apply this to two datasets: (i) EEG recordings from a general anesthesia protocol, and (ii) EEG recordings acquired in the resting state from healthy control subjects and individuals with inattentive-type attention deficit hyperactivity disorder (ADHD), including eyes-open and eyes-closed conditions. We find that the permutation entropy distributions exhibit a clear dependence on brain state. In particular, conscious, inattentive-type ADHD, and eyes closed conditions show lower mean values and larger standard deviations of permutation entropy. To evaluate the discriminative power of permutation entropy, we train classification models using permutation entropy as an input feature. The results show that the distinction between conscious and unconscious states can be reliably captured in the general-anesthesia dataset. In the resting-state dataset, eyes-open and eyes-closed conditions are distinguishable, whereas classification between control and inattentive-type ADHD groups does not show clear separability. This indicates that information not captured in ordinal patterns, such as the original time-series values, may play a more crucial role in detecting inattentive-type ADHD. Our findings demonstrate that permutation entropy derived from EEG phase dynamics provides an effective indicator of brain states, particularly in relation to consciousness, while also highlighting its limitations for identifying individuals with inattentive-type ADHD.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript analyzes EEG signals by extracting the principal mode of phase dynamics (intended to capture anterior-posterior information flow) and computing permutation entropy on its ordinal patterns. It applies this to a general-anesthesia dataset and a resting-state dataset (controls vs. inattentive-type ADHD, eyes-open vs. eyes-closed). The central claims are that permutation-entropy distributions differ by brain state (lower mean, larger SD for conscious/ADHD/eyes-closed), that classification models trained on these values reliably separate conscious from unconscious states and eyes-open from eyes-closed, but fail to separate ADHD from controls, implying that amplitude information (absent from ordinal patterns) is required for the ADHD distinction.

Significance. If the reported classification results can be substantiated with quantitative metrics, the work would supply a computationally lightweight, ordinal-pattern-based marker for consciousness level that avoids direct use of signal amplitude. The negative ADHD result would then usefully highlight a limitation of phase-only ordinal analysis. At present the absence of performance numbers prevents any assessment of practical utility or comparison with existing EEG complexity or connectivity markers.

major comments (3)
  1. [Results] Results section (classification experiments): the statements that conscious/unconscious states 'can be reliably captured' and that eyes-open/closed conditions 'are distinguishable' are unsupported by any reported accuracy, AUC, F1, confusion matrix, cross-validation scheme, or statistical test; without these quantities the distributional differences cannot be translated into a claim of discriminative power.
  2. [Methods] Methods (phase-dynamics extraction and permutation-entropy calculation): the procedure for isolating the 'principal mode of EEG phase dynamics' is described only at a high level; no explicit algorithm, decomposition method, or definition of anterior-posterior flow is given, rendering both the positive consciousness results and the negative ADHD conclusion non-reproducible and preventing evaluation of whether amplitude information was truly excluded.
  3. [Abstract / Results] Abstract and Results: the claim that 'information not captured in ordinal patterns, such as the original time-series values, may play a more crucial role in detecting inattentive-type ADHD' is an interpretation of a negative classification result; because no positive performance metrics are supplied for the other tasks, it is impossible to judge whether the ADHD failure is due to the feature choice or to insufficient model capacity or data quality.
minor comments (2)
  1. [Abstract] The abstract states that 'permutation entropy distributions exhibit a clear dependence on brain state' yet supplies no numerical means, standard deviations, or p-values; these should be added to the corresponding results paragraph or table.
  2. [Methods / Datasets] No statement is made about the number of subjects, channels, or recording lengths in either dataset; these basic sample-size details are required for interpreting the reported standard deviations of permutation entropy.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments. The feedback correctly identifies that quantitative classification metrics and expanded methodological details are needed to support the claims. We will revise the manuscript to address these issues directly.

read point-by-point responses

  1. Referee: [Results] Results section (classification experiments): the statements that conscious/unconscious states 'can be reliably captured' and that eyes-open/closed conditions 'are distinguishable' are unsupported by any reported accuracy, AUC, F1, confusion matrix, cross-validation scheme, or statistical test; without these quantities the distributional differences cannot be translated into a claim of discriminative power.

    Authors: We agree that the current presentation relies on distributional differences without reporting performance metrics. In the revised manuscript we will add accuracy, AUC, F1, confusion matrices, a description of the cross-validation scheme, and appropriate statistical tests for the consciousness and eyes-open/closed distinctions. revision: yes

  2. Referee: [Methods] Methods (phase-dynamics extraction and permutation-entropy calculation): the procedure for isolating the 'principal mode of EEG phase dynamics' is described only at a high level; no explicit algorithm, decomposition method, or definition of anterior-posterior flow is given, rendering both the positive consciousness results and the negative ADHD conclusion non-reproducible and preventing evaluation of whether amplitude information was truly excluded.

    Authors: We acknowledge the description is high-level. The revised Methods section will supply the explicit algorithm, the decomposition technique employed to isolate the principal mode, and a precise definition of the anterior-posterior flow measure, thereby allowing full reproducibility and confirmation that amplitude information was excluded. revision: yes

  3. Referee: [Abstract / Results] Abstract and Results: the claim that 'information not captured in ordinal patterns, such as the original time-series values, may play a more crucial role in detecting inattentive-type ADHD' is an interpretation of a negative classification result; because no positive performance metrics are supplied for the other tasks, it is impossible to judge whether the ADHD failure is due to the feature choice or to insufficient model capacity or data quality.

    Authors: We accept that the interpretation cannot be fully evaluated without the missing metrics. Once the quantitative results for the consciousness and eyes-open/closed tasks are added (as noted in the first response), the negative ADHD outcome can be placed in proper context; the revised text will qualify the interpretation accordingly. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical application of pre-defined measure with no derivations or self-referential reductions

full rationale

The paper applies the standard permutation entropy measure (a known ordinal-pattern complexity metric) to the principal mode of EEG phase dynamics extracted from two datasets. No equations, derivations, or parameter-fitting steps are described that would reduce any reported classification performance or separability claim to a fitted input or self-citation chain. The work consists of straightforward group comparisons and model training on the pre-computed entropy values; the negative ADHD result and positive consciousness/eyes-open-closed results are presented as direct empirical outcomes without any load-bearing self-definition or renaming of known results. This is the most common honest non-finding for purely empirical studies.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The analysis rests on the standard mathematical definition of permutation entropy applied to ordinal patterns extracted from a principal phase mode; no free parameters, ad-hoc axioms, or new entities are introduced in the abstract.

axioms (1)
  • standard math Permutation entropy computed on ordinal patterns of a time series is a valid scalar measure of its disorder.
    Invoked when the authors quantify disorder of EEG phase dynamics.

pith-pipeline@v0.9.0 · 5843 in / 1378 out tokens · 51644 ms · 2026-05-23T00:33:07.370444+00:00 · methodology

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Forward citations

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Reference graph

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