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iFuzz-Meta: An Interpretable Fuzzy Learning Framework Bridging Top-Down and Bottom-Up Knowledge Integration

Xiaowei Jiang, Daniel Leong, Beining Cao, Nan Zhou, Yingtao Ren, Yu-Cheng Chang, Thomas Do, Chin-Teng Lin

arXiv:2608.14646v1cs.LGcs.AIcs.HC

TL;DR

Interpretable fuzzy learning still struggles to preserve human-understandable reasoning in modern neural representations. iFuzz-Meta defines semantically grounded fuzzy prototypes, analyzes their adaptation across tasks through meta-learning, and integrates theoretical priors with data-driven refinement; experiments show improved generalization when sufficient data is available.

  • Problem

    Interpretable representation learning remains limited by opaque neural representations and fuzzy prototypes defined in high-dimensional latent spaces.

  • Method

    iFuzz-Meta combines raw-space semantic prototypes, meta-learning across cognitive tasks, and knowledge-guided regularization as soft top-down constraints.

  • Results

    Frequency-domain regularization consistently improves accuracy across tasks and model structures at FSR = 0.3, with benefits less consistent at FSR = 0.1.

  • Takeaways & Limitations

    The framework advances interpretable fuzzy systems by linking transparent rule-based reasoning with adaptive cross-task analysis and soft knowledge integration.

  • Takeaways & Limitations

    Sparse rule representations may underrepresent synergistic relationships among rules, limiting flexibility in complex or overlapping domains.

Abstract

from arXiv · show

Interpretable representation learning remains a key challenge in modern neural computation, particularly when models are expected not only to perform but also to explain their reasoning. This paper introduces iFuzz-Meta, an interpretable fuzzy rule-based learning framework that preserves human-understandable reasoning structures within modern neural architectures. Each fuzzy rule corresponds to a semantic and spatial prototype defined in the original feature space, enabling transparent inference and direct interpretability. Meta-learning is employed as an analytical paradigm to examine how these interpretable rules reorganize across tasks and domains, providing a principled means to link algorithmic adaptation with cognitive representation. A knowledge-guided regularization mechanism further enables a top-down-bottom-up integration, in which theoretical priors act as soft inductive biases while data-driven learning refines and extends them. This dual process ensures that adaptation proceeds along semantically and physiologically meaningful trajectories, rather than arbitrary parameter shifts. Evaluations demonstrate that iFuzz-Meta achieves interpretable reasoning and stable cross-domain generalization, establishing a potential general pathway toward explainable and knowledge-aware fuzzy systems.

I. INTRODUCTION

iFuzz-Meta addresses the opacity and weak semantic grounding of modern neural and fuzzy-neural models by defining interpretable fuzzy prototypes in the original EEG signal space while performing inference in latent space. It uses cross-subject meta-learning and soft knowledge-guided regularization to study transferable representational changes and integrate top-down constraints with bottom-up adaptation.

  • Motivation: Modern neural models achieve strong predictive performance but learn entangled representations that are difficult to trace to semantic concepts or domain knowledge.Post-hoc methods such as SHAP and Grad-CAM do not guarantee causal validity or provide reusable task-level knowledge.
  • Proposed framework: iFuzz-Meta assigns each fuzzy rule a spatially grounded, semantically meaningful prototype defined in the original EEG signal space.This design is intended to preserve human-understandable reasoning structures while revealing how they evolve across cognitive tasks.
  • Proposed framework: Unlike latent-space fuzzy prototypes in established fuzzy neural networks, iFuzz-Meta defines centers in raw space and performs fuzzy inference in latent space.The framework thereby combines semantic interpretability with neural representation learning.
  • Meta-learning: Meta-learning analyzes cross-subject few-shot adaptation under LOSO through object recognition and object identification, linking categorical processing with instance-specific individuation in neural and rule space.These cognitively grounded meta-tasks are used to distill intention-related regularities shared across subjects and learn transferable fuzzy rule priors.
  • Knowledge integration: A frequency-regularized center loss implements soft knowledge-guided regularization, allowing theoretical constraints and task-driven losses to jointly shape updates of the same fuzzy prototypes.This top-down and bottom-up interaction guides learning toward physiologically and semantically meaningful representations while supporting stability, transferability, and interpretability.

II. METHODS · A. Problem Formulation and Meta-Task Construction · 1) Data Representation and Task Setup:

The method represents each EEG trial as a labeled multichannel time series and evaluates adaptation to an unseen target subject through LOSO few-shot meta-learning. It models target-specific variability as deviations from shared structure and adapts fuzzy centers and decision layers using limited labeled support data.

  • 1) Data Representation and Task Setup:: Each EEG trial is represented as a multichannel time series x ∈ R^C×T with C = 60 channels and T = 1000 time points sampled over 1 second.Recordings come from the OR and OI paradigms.
  • 1) Data Representation and Task Setup:: Trials are annotated with labels y ∈ {1, 2, 3, 4}, corresponding to four object classes.
  • 1) Data Representation and Task Setup:: The classifier fθ : R^C×T → {1, 2, 3, 4} is trained on data from N − 1 source subjects to generalize to an unseen target subject.
  • 1) Data Representation and Task Setup:: Cross-subject generalization is evaluated with a leave-one-subject-out few-shot meta-learning protocol.Each evaluation episode partitions data into target-subject adaptation and evaluation subsets.
  • 1) Data Representation and Task Setup:: Base subjects provide shared cognitive and neurophysiological priors for constructing a transferable feature space.
  • 1) Data Representation and Task Setup:: Target-subject variability δ^(t) may be systematic, such as head geometry and electrode impedance, or stochastic, such as arousal and attention.
  • 1) Data Representation and Task Setup:: Given a latent encoder p0 : R^C×T → R^D, target EEG is modeled as shared latent structure plus subject-specific deviation, and few-shot adaptation fine-tunes fuzzy centers and decision layers.Adaptation uses only a small labeled target support set, |D_support^(t)| ≪ |D_t|.

2) From Meta-Tasks to Class-Level Generalization:

The section defines OI- and OR-based meta-tasks to separate instance-level specificity from category-level invariance. A bi-level optimization then promotes class-discriminative, subject-invariant representations and rapid subject-specific adaptation for low-resource EEG generalization.

  • Meta-task construction: OI distinguishes visually similar object instances, whereas OR performs abstract semantic categorization across different cognitive abstraction levels.These paradigms provide structured learning signals for the task distribution T = {τk}.
  • Meta-task construction: The meta-task setup encourages feature hierarchies that disentangle instance-level specificity from category-level invariance, enabling semantic generalization across subjects and paradigms.
  • Bi-level meta-optimization: Learning uses a bi-level meta-optimization with task-specific inner-loop updates followed by outer-loop evaluation on corresponding query sets.The inner loop updates θ using task loss Lτ, while the outer loop evaluates the adapted parameters θ′.
  • Generalization objective: The meta-learner separates transferable priors from subject-dependent variability, supporting robust cross-subject generalization and rapid subject-specific adaptation in low-resource EEG settings.The encoder p0 is trained to extract class-discriminative and subject-invariant representations through the update θ 7→θ′.

B. Model Architecture Overview · C. Fuzzy Inference System (FIS) Foundations · 1) Mamdani-style Inference for Neural Modulation:

iFuzz-Meta projects EEG signals into latent features and applies Mamdani-style fuzzy reasoning through explicit rules, Gaussian prototypes, and OR-operation defuzzification. This design selects a dominant rule pathway to produce an interpretable neural modulation response while preserving computational tractability.

  • B. Model Architecture Overview: The encoder p0 projects raw EEG signals from R^Draw into an R^D latent feature space suited to fuzzy reasoning.The implementation follows baseline and uses EEGNet because of its neural signal decoding effectiveness and compact, interpretable architecture.
  • B. Model Architecture Overview: Three p0 frameworks capture progressively abstract neural representations for flexible integration with the fuzzy module across processing stages.These variants support different levels of neural representation.
  • C. Fuzzy Inference System (FIS) Foundations: The fuzzy inference system attributes each input to an explicit interpretable fuzzy rule rather than aggregating information from multiple latent components.It follows a Mamdani-style paradigm comprising fuzzification, rule evaluation, and defuzzification.
  • 1) Mamdani-style Inference for Neural Modulation:: Each rule follows the form “IF x is Ar THEN y is Br,” where x is the latent input, Ar the antecedent set, and Br the consequent.The latent input satisfies x ∈ R^D.
  • 1) Mamdani-style Inference for Neural Modulation:: The antecedent fuzzy set uses a Gaussian membership function centered at prototype cr with width wr.This parameterization defines the rule’s membership response in the latent space.
  • 1) Mamdani-style Inference for Neural Modulation:: OR-operation defuzzification selects the rule with the highest membership value µr(x) at dimension d to determine the final modulation response as a gating attention vector.The method avoids aggregating outputs from all rules.
  • 1) Mamdani-style Inference for Neural Modulation:: Selecting one dominant rule pathway for each input enhances interpretability while maintaining computational tractability within a neural framework.This preserves the core principles of Mamdani inference.

D. iFuzzy Network Module · 1) Fuzzy Rule Representation:

iFuzz-Meta represents each fuzzy rule with interpretable prototypes defined in raw EEG space and projects them into a shared latent space for task-adaptive fuzzy inference. This preserves a traceable connection between rule activations and concrete neurophysiological patterns while maintaining consistent latent geometry.

  • D. iFuzzy Network Module: Each fuzzy rule is parameterized by learnable center and width prototypes in the raw input space.These prototypes act as semantically meaningful anchors for interpretable fuzzy reasoning.
  • D. iFuzzy Network Module: Raw-space prototypes encode inductive biases such as spectral priors before being transformed into task-adaptive latent representations.A shared projector maps the prototypes into the model’s latent space before fuzzy inference.
  • D. iFuzzy Network Module: Unlike attention similarity weights, fuzzy membership compares projected EEG signals with prototypes explicitly defined in raw EEG signal space.This design grounds rule activation in interpretable neurophysiological patterns rather than unstructured latent similarity.
  • D. iFuzzy Network Module: Each rule is semantically defined in the original raw signal space and operationally instantiated in latent space through projector p0.The projection links semantic rule definitions to the representations used for computation.
  • 1) Fuzzy Rule Representation:: Latent fuzzy centers cr and widths wr are deterministic projections of interpretable raw-space prototypes, not independently learned embedding-space parameters.The transformed parameters cr and wr are used in subsequent fuzzy inference computations.
  • 1) Fuzzy Rule Representation:: A shared projection function p0 maps both inputs and fuzzy prototypes, preserving consistent latent geometry and traceability to raw EEG rule definitions.The paper notes that dual-projection ablation details appear in Supplementary Section II-E.

2) Rule Aggregation and Firing Computation: · 3) Knowledge-Guided Modulation via Rule Activation:

The framework computes Gaussian rule memberships in latent space, selects a single maximally activated prototype for sparse Mamdani-style inference, and uses its firing strength to gate input features. This preserves rule-level interpretability while focusing gradients and modulation on semantically supported representations.

  • 2) Rule Aggregation and Firing Computation:: Each input feature embedding z is evaluated against every fuzzy rule using a Gaussian membership function in the latent space.Rule centers and widths are projected into the latent space, with ε ≪ 1 added for numerical stability.
  • 2) Rule Aggregation and Firing Computation:: The model replaces traditional all-rule Mamdani aggregation with an OR operation whose firing strength equals each rule’s Gaussian membership value.This produces sparse, interpretable reasoning rather than a composite inference surface.
  • 2) Rule Aggregation and Firing Computation:: The rule with maximum activation governs Mamdani-style max-membership defuzzification, assigning each decision to a single activated prototype.Single-rule inference reduces semantic ambiguity and improves computational efficiency during training and inference.
  • 2) Rule Aggregation and Firing Computation:: The OR mechanism creates sparse gradient flow that reduces inter-rule redundancy and concentrates learning on discriminative prototypes.This sharpens attention over relevant latent-space regions and supports scalable, semantically grounded reasoning.
  • 3) Knowledge-Guided Modulation via Rule Activation:: After selecting rule r*, the network uses its firing strength α = µr*(z) as a continuous signal for element-wise multiplicative feature gating.The modulation coefficient is λ = 0.1, and α ranges from 0 to 1.
  • 3) Knowledge-Guided Modulation via Rule Activation:: Fuzzy attention adapts modulation intensity to input–prototype alignment, preserving the baseline representation at low α and emphasizing supported dimensions at high α.Thus, feature enhancement depends on the selected rule’s confidence.
  • 3) Knowledge-Guided Modulation via Rule Activation:: Unlike traditional attention, fuzzy modulation directly links feature enhancement to explicit rule activations for localized, semantically grounded representation updates.The gradient computation used during training is described in Supplemental Material Section I-B.

E. Loss Function Design · 1) Frequency-Regularized Center Loss:

The model combines supervised cross-entropy classification with auxiliary domain-informed losses, including a frequency-regularized center loss that constrains fuzzy prototypes toward physiologically meaningful EEG spectra. This transparent spectral prior suppresses noise-dominated frequencies while supporting interpretable and transferable representations.

  • E. Loss Function Design: Supervised classification uses standard cross-entropy loss between predicted logits and ground-truth labels.The same formulation is used during meta-learning with two labels and fine-tuning with four labels.
  • E. Loss Function Design: Two auxiliary losses impose domain-informed inductive biases on the learned fuzzy parameters.These losses complement the primary supervised classification objective.
  • 1) Frequency-Regularized Center Loss:: The frequency-regularized center loss imposes physiologically meaningful temporal structure by penalizing low- and high-frequency components outside a target EEG band.The target band is associated with informative rhythms such as alpha and beta.
  • 1) Frequency-Regularized Center Loss:: For each fuzzy rule, the method transforms the raw center across channels and time points into the frequency domain before computing spectral regularization.The raw center is represented as ˜c_r ∈ R^{C×T}, and its discrete Fourier transform is denoted ˆc_r(f).
  • 1) Frequency-Regularized Center Loss:: The regularizer defines a target passband [f_min, f_max] and applies a frequency-dependent weighting function to spectral components.The passband specifies the frequencies intended to remain unpenalized or less penalized.
  • 1) Frequency-Regularized Center Loss:: The weighting is symmetric and band-aware: in-band frequencies are unpenalized, whereas out-of-band frequencies receive soft penalties based on distance from band boundaries.This design avoids a hard cutoff while encouraging concentration within the designated band.
  • 1) Frequency-Regularized Center Loss:: The spectral loss fixes the minimum analyzed frequency at 0.5 Hz and suppresses frequencies outside the functional EEG range while preserving components associated with cognitive activity.The formulation explicitly incorporates neuroscientific priors into each fuzzy center.
  • 1) Frequency-Regularized Center Loss:: By constraining spectral profiles rather than relying only on convolutional filters, the method provides a transparent, tunable route for knowledge transfer that supports physiologically interpretable patterns and cross-subject few-shot robustness.The constraint is described as reducing subject-specific noise and promoting shared low-frequency, task-relevant structures.

2) Total loss: · III. RESULTS · A. Meta-Learning Performance Evaluation

The total objective combines classification, frequency-regularization, and diversity losses, while meta-learning evaluation tests iFuzz-Meta across few-shot ratios, architectures, and training regimes. Across both few-shot settings, all iFuzz-Meta variants outperform EEGNet and EEGformer during meta-learning, without significant differences versus EEG-TCNet or among the variants.

  • 2) Total loss:: The final loss is a weighted sum of classification, frequency-regularization, and diversity components.Ltotal = LCE + λfreq · Lfreq + λdiv · Ldiv.
  • 2) Total loss:: The frequency and diversity coefficients are both defined as 0.3, providing weak structural guidance while preserving fuzzy-representation flexibility.These terms control frequency regularization and the diversity prior.
  • 2) Total loss:: Prior-informed and task-supervision gradients jointly update shared prototype parameters, allowing data-driven learning to modulate the prior’s effective influence.The integration is realized through coupled gradients acting on the same prototype parameters.
  • III. RESULTS: Classification performance is evaluated at FSR = 0.1 and 0.3 across TSPA, TSpPA, and TSSPA architectures, with and without meta-learning.The experiments use the EEGNet setup and compare against EEGNet, EEGformer, EEG-TCNet, and two Deep FNN models.
  • A. Meta-Learning Performance Evaluation: All three iFuzz-Meta structures significantly outperform EEGNET and EEGformer during meta-learning at both few-shot ratios.The reported significance threshold is p < 0.05.
  • A. Meta-Learning Performance Evaluation: No significant differences occur between iFuzz-Meta variants, between FSR = 0.1 and FSR = 0.3, or versus EEG-TCNet during meta-learning.The comparisons with EEG-TCNet and among variants report p > 0.05.

B. Overall Performance Comparison … 1) Interpretable Representations through Architecture Design:

Across overall comparisons and ablations, iFuzz-Meta combines competitive classification with interpretable fuzzy reasoning, while meta-learning and frequency-domain priors improve performance under specified conditions. Architectural analysis further shows that TSSPA produces the most structured and interpretable prototypes despite similar classification performance across architectures.

  • B. Overall Performance Comparison: With meta-learning, iFuzz-Meta consistently outperforms EEGformer, EEG-TCNet, and EEGNet across both FSR settings (all p < 0.05).Without meta-learning, it exceeds EEG-TCNET except for TSSPA in the OR task with FSR = 0.1 (p > 0.05).
  • B. Overall Performance Comparison: Meta-learning generally improves performance, with significant gains especially for TSpPA and TSSPA in the OR task under FSR = 0.1 (p < 0.05).Under FSR = 0.3, improvements are more selective; only TSpPA shows no significant OI-task gain (p > 0.05).
  • B. Overall Performance Comparison: The framework achieves both interpretability and competitive performance, with comparisons to deep fuzzy neural networks and convergence analysis reported in supplemental sections.The main comparisons use four fuzzy rules across OI and OR paradigms under meta-learning and non-meta-learning conditions.
  • C. Ablation Study: Frequency-regularized center loss consistently improves accuracy under FSR = 0.3 across all tasks and model structures (p < 0.001).The loss constrains fuzzy prototypes in the frequency domain using neuroscientific priors.
  • C. Ablation Study: Under FSR = 0.1, the frequency-domain prior improves some cases, including TSSPA in the OI task (+1.41%, p < 0.01), while others remain comparable or slightly lower (p > 0.05).The reported pattern suggests stronger benefits as the support set grows.
  • C. Ablation Study: The proposed loss embeds domain knowledge into fuzzy representations through interpretable, physiologically meaningful regularization that improves generalization especially with sufficient data.This contrasts with architectures relying purely on data-driven learning.
  • 1) Interpretable Representations through Architecture Design:: TSPA emphasizes temporal structure, TSpPA captures smoother spatial activations, and TSSPA combines spatial precision with temporal dynamics in fuzzy center prototypes.These differences arise from the architectures’ distinct temporal and spatial processing orders.
  • 1) Interpretable Representations through Architecture Design:: Although the architectures achieve similar classification performance, TSSPA yields more structured and interpretable prototypes that often resemble canonical EEG structures such as ERPs.The result links architectural priors with meaningful rule learning and alignment with neurophysiological patterns.

2) Center Shifting from Meta-Learning to Fine-Tuning: · 3) Prototype Differences With vs. Without Meta-Learning:

Meta-learning produces dispersed, task-agnostic prototypes that fine-tuning reshapes into smoother, task-specific spatial filters. Compared with training from scratch, meta-learned initialization yields more coherent prototypes, stronger structural alignment, and better out-of-domain performance.

  • 2) Center Shifting from Meta-Learning to Fine-Tuning:: Meta-training learns dispersed and irregular spectral-spatial centers because it captures transferable priors across OI and OR tasks with differing granularity and abstraction.The variability reflects task-agnostic representation learning rather than task-specific feature fitting.
  • 2) Center Shifting from Meta-Learning to Fine-Tuning:: Subject-specific fine-tuning converts generic meta-learned centers into smoother, spatially consistent filters aligned with localized cognitive-task organization.The reported smoothness is more prominent at broader semantic abstraction.
  • 2) Center Shifting from Meta-Learning to Fine-Tuning:: Table IV evaluates iFuzzy accuracy with and without knowledge-based frequency-regularized center loss across few-shot ratios, task types, and model layers.Values are reported as mean ± standard deviation, with significance markers for improvements over the No-Frequency loss condition.
  • 2) Center Shifting from Meta-Learning to Fine-Tuning:: TSSPA therefore supports two-phase learning: general representation acquisition followed by task specialization.This separates transferable representation learning from subject-specific adaptation.
  • 3) Prototype Differences With vs. Without Meta-Learning:: Without meta-learning, fine-tuning from scratch produces spatially fragmented and inconsistent centers, indicating difficulty organizing the latent space from an unstructured initialization.This comparison uses the bottom-row topographies in Fig. 8(A).
  • 3) Prototype Differences With vs. Without Meta-Learning:: Meta-learned initialization produces more coherent, interpretable prototypes and supports stable optimization trajectories that reuse cognitively meaningful patterns across tasks.The meta-learned priors act as structured initialization for subsequent fine-tuning.
  • 3) Prototype Differences With vs. Without Meta-Learning:: In UMAP, meta-learned centers attract fine-tuned representations, whereas removing these anchors scatters distributions and weakens alignment across subjects or tasks.These observations link meta-learning to structural consistency as well as generalization.
  • 3) Prototype Differences With vs. Without Meta-Learning:: An out-of-domain validation on a public multi-day motor imagery EEG dataset found consistently better iFuzz-Meta performance than the baselines.The dataset uses a neural paradigm fundamentally different from the original OI/OR tasks; detailed settings and results appear in Supplemental Material Section II-D.

IV. DISCUSSION … C. Hierarchical Task Design Enables Intention-Aware Meta-Learning

iFuzz-Meta learns neurophysiologically plausible fuzzy prototypes that remain stable across task adaptation, while hierarchical meta-learning models the OR→OI shift from semantic recognition to instance individuation. This design preserves interpretable reasoning, supports cross-task continuity, and guides intention-relevant feature learning.

  • A. Neural Interpretation of Fuzzy Center Prototypes: The learned prototypes are neurophysiologically plausible, with spatially localized and temporally distinct structures that make EEG features human-understandable.Their structure is described as consistent with established cognitive components, linking decoding accuracy with neurophysiological insight.
  • B. Center Prototype Consistency and Microstate–Level Interpretation: Rule #4 has the smallest adaptation distance across meta-learning, OR, and OI, indicating stability under domain adaptation.Its bilateral fronto-parietal activation band with a midline transition is characteristic of EEG microstate C, associated with higher-order cognition and self-referential processing.
  • B. Center Prototype Consistency and Microstate–Level Interpretation: Microstate C links self-referential and personally significant information with externally directed cognitive control through interactions involving default-mode and cognitive-control systems.The passage also reports functional connectivity with cingulo-opercular, dorsal attention, and default-mode networks at rest.
  • B. Center Prototype Consistency and Microstate–Level Interpretation: Across tasks, Rule #4 implies a stable control mechanism that maintains cognitive continuity as processing shifts from category-level recognition to instance-level specification.This connects the observed rule stability to continuity across the OR-to-OI processing transition.
  • B. Center Prototype Consistency and Microstate–Level Interpretation: Rule #4 functions as a task-invariant anchor in rule space, preserving interpretable reasoning while adapting across tasks and aligning algorithmic stability with neurophysiological consistency.The passage characterizes this minimal domain shift as evidence of a shared fuzzy prototype aligned with a stable neurocognitive microstate.
  • C. Hierarchical Task Design Enables Intention-Aware Meta-Learning: The OR→OI transition reflects a cognitive hierarchy from semantic categorization toward fine-grained individuation supported by distinct visual and attentional mechanisms.OR emphasizes higher-order semantic categorization, whereas OI requires occipito-parietal attention and ventral-stream perceptual detail.
  • C. Hierarchical Task Design Enables Intention-Aware Meta-Learning: Modeling OR→OI as a binary meta-task encourages intention-relevant feature refinement from category to instance and reuses meta-learned features for four-way OR and OI decoding.The hierarchical structure is reported to improve cross-subject sample efficiency and align fuzzy prototypes with semantic and individuation stages.

D. Balancing Interpretability and Model Capacity … V. CONCLUSION

iFuzz-Meta balances interpretability and model capacity through neurophysiologically grounded fuzzy prototypes, knowledge-guided learning, and smoothly evolving rule activations. The framework remains limited by sparse single-rule representations, motivating broader modalities and multi-rule extensions while preserving transparent reasoning.

  • D. Balancing Interpretability and Model Capacity: iFuzz-Meta constrains decisions through a limited set of fuzzy rules linked to human-interpretable spatiotemporal EEG prototypes.These prototypes act as semantic anchors for classification and neuroscientific interpretation.
  • D. Balancing Interpretability and Model Capacity: The fuzzy module translates distributed neural signals into rule-driven reasoning that is data-aligned and theory-consistent, supporting explanations in BCI contexts.This addresses applications where understanding how decisions are made can be as important as the decisions themselves.
  • D. Balancing Interpretability and Model Capacity: Physiological priors and diversity losses guide fuzzy centers toward meaningful EEG rhythms, separated functional motifs, biological plausibility, and cross-subject transferability.The priors guide rather than rigidly impose structure, while centers and rules are refined through learning.
  • D. Balancing Interpretability and Model Capacity: Low-dimensional, smoothly evolving rule activations support real-time BCI control, continuous cognitive-state monitoring, and adaptive neurofeedback.They provide transparency and robustness that are difficult to achieve with purely deep architectures.
  • D. Balancing Interpretability and Model Capacity: Unlike attention mechanisms, iFuzz-Meta defines each fuzzy rule as a neurophysiologically grounded prototype in raw signal space rather than an optimized discriminative similarity.Its inference is consequently tied to physically interpretable EEG structure.
  • E. Limitations: Sparse single-rule activation may underrepresent synergistic relationships among rules, constraining flexibility in complex or overlapping domains; hierarchical or multi-rule routing is proposed as a remedy.The limitation concerns secondary yet informative contributions that sparsity may neglect.
  • F. Future Work: Future work will extend evaluation beyond EEG to fNIRS and fMRI, explore transparent multi-rule synergy, and investigate richer knowledge integration.These directions target generality across measurement principles and spatiotemporal resolutions while retaining human-understandable reasoning.
  • V. CONCLUSION: The study presents iFuzz-Meta as an interpretable fuzzy learning framework unifying rule-based reasoning with adaptive meta-learning and preserving prototypes in the original feature space.Meta-learning is framed as a cognitive-level analytical process for examining rule evolution across domains and tasks.
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