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Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Eeg Diffusion Visual ReconstructionStructure-Guided Diffusion Model (SGDM) for EEG-based visual cognition reconstruction. Combines structurally supervised VAE, spatiotemporal EEG encoder with contrastive learning, and ControlNet-guided diffusion for high-fidelity visual reconstruction from brain signals. Activation: EEG visual reconstruction, brain-computer interface image generation, SGDM, neural decoding visual.Votes: 0GitHub stars: 3
- Eeg Digital Twin Autonomous DrivingEEG-fused digital twin brain framework for autonomous driving in virtual scenarios. Combines biophysical brain models with EEG data and digital twin technology for driver state monitoring and vehicle control. Applies to: brain-computer interfaces, autonomous driving, driver monitoring, digital twin brains. Activation: eeg digital twin, autonomous driving brain, driver state monitoring, virtual scenario brain, EEG-fused brain model.Votes: 0GitHub stars: 3
- Eeg Emergency Braking BssEEG-based emergency braking intensity prediction using blind source separation. Artifact removal for reliable EEG-based driver assistance systems. Activation: eeg braking, blind source separation, driver assistance, artifact removal.Votes: 0GitHub stars: 3
- Eeg Faar Artifact RejectionFast Automatic Artifact Rejection (FAAR) methodology for EEG motor imagery BCIs. Lightweight automated artifact rejection that computes artifact-sensitive features, derives epoch-level Signal Quality Index, adaptively selects rejection thresholds, and automatically rejects contaminated epochs. Reduces inter-subject variability and addresses BCI illiteracy. arXiv: 2605.12408 (eess.SP). Hajhassani, Aristimunha, Graignic, Mellot, Kusch, Delorme, Semah, Caillet.Votes: 0GitHub stars: 3
- Eeg Fm Audit Systematic EvaluationEEG基础模型系统评估和分析管道。提出ASHA基准测试、范式级消融研究、神经生理学探测(NPP)框架，确保EEG基础模型的公平评估和可解释性。Votes: 0GitHub stars: 3
- Eeg Fmri Spatiotemporal Neural FramesEEG-conditioned framework for reconstructing dynamic fMRI as continuous neural sequences with high spatial fidelity and temporal coherence at cortical-vertex level. Incorporates null-space intermediate-frame reconstruction for handling sampling irregularities.Votes: 0GitHub stars: 3
- Eeg Foundation Lrp InterpretabilityLayer-wise Relevance Propagation (LRP) methodology for interpreting EEG foundation models. Extends LRP from CNN-based to Transformer-based EEG models, enabling verification and hypothesis discovery. Activation: EEG interpretability, LRP, EEG foundation model, transformer attribution, Clever Hans EEG, post-hoc attribution.Votes: 0GitHub stars: 3
- Eeg Foundation Model AdaptersEEG foundation models with domain adaptation using lightweight adapters. Covers pre-trained EEG encoders, task-specific fine-tuning with adapters, cross-dataset generalization, and efficient deployment. Use when working with EEG foundation models, neural signal pre-training, adapter-based fine-tuning, or cross-dataset EEG classification.Votes: 0GitHub stars: 3
- Eeg Foundation Model Burst Suppression IcuFirst comprehensive evaluation of EEG Foundation Models (FMs) for burst suppression detection in reduced-montage ICU EEG without patient-specific calibration. **REVE-base achieves highest event-based F1-score (0.868)**, reducing burst-per-minute error by 52.1% and 36.2% compared to EEGNet and adaptive thresholding respectively. Demonstrates FMs enable scalable EEG monitoring in clinical settings.Votes: 0GitHub stars: 3
- Eeg Foundation Sae InterpretabilityMechanistic interpretability of EEG foundation models using Sparse Autoencoders (SAEs). Extracts interpretable feature dictionaries from EEG transformer embeddings via TopK SAEs, benchmarks monosemanticity across architectures (SleepFM, REVE, LaBraM), and introduces concept steering with target vs. off-target probe metrics. Use when: interpreting EEG models, sparse autoencoders for neural data, EEG foundation model analysis, mechanistic interpretability of time-series models, concept steering...Votes: 0GitHub stars: 3
- Eeg Foundation Temporal Correlations BlindnessEEG foundation models lose long-range temporal correlations: framework for analyzing spectral-temporal dissociation and cross-population fragility in EEG foundation models. Provides methodology for testing LRTC recovery via DFA exponent and evaluating cross-cohort transfer performance.Votes: 0GitHub stars: 3
- Eeg Hopfield Emotion Energy LandscapesEEG-based Hopfield energy landscape analysis for quantifying brain network stability during emotional processing (happy/sad face tasks). Activation: emotion energy landscape, brain stability, happy sad face EEG, Hopfield emotion.Votes: 0GitHub stars: 3
- Eeg Hopfield Emotion EnergyEnergy landscapes for quantifying brain network stability during emotional processing. Uses Hopfield network energy framework to analyze EEG dynamics, mapping emotional states to attractor basins in brain network energy landscapes. Provides a physics-based framework for understanding emotional stability and transitions. Activation: energy landscape, Hopfield network emotion, brain network stability, emotional processing EEG, attractor dynamics, affective neuroscience, 能量景观, 情绪脑网络, 吸引子动力学Votes: 0GitHub stars: 3
- Eeg Ieeg Bridge BciBridging scalp EEG and intracranial EEG (iEEG) in BCI via pretrained neural models. Maps non-invasive scalp EEG to iEEG-quality representations, enabling high-fidelity BCI without invasive implants. Uses pretrained models to learn the scalp-to-cortical mapping. Activation: EEG iEEG bridge, scalp to intracranial, BCI translation, non-invasive BCI, cortical reconstruction, EEG-to-iEEG, 脑电皮层映射, 无创BCIVotes: 0GitHub stars: 3
- Eeg Meg Brain Network AnalysisSkill for analyzing brain networks using noninvasive electrophysiological measurements (EEG/MEG) based on arXiv:2607.17602v1 'Exploring Brain Networks Using Noninvasive Electrophysiological Measurements: Methods and Applications'Votes: 0GitHub stars: 3
- Eeg Mftnet Multi Scale TemporalEEG-MFTNet: Enhanced EEGNet with multi-scale temporal convolution and fusion transformer for cross-session motor imagery decoding. Addresses session variability in BCI through dual-branch architecture combining frequency-specific temporal features with global temporal modeling.Votes: 0GitHub stars: 3
- Eeg Microstate Tokenizer RepresentationUniversal EEG microstate tokenizer for representation learning across downstream tasks. Clusters continuous EEG into discrete microstate tokens that serve as universal building blocks for sleep staging, emotion recognition, seizure detection, and motor imagery. Provides a tokenization-first approach to EEG encoding, distinct from variational embedding or feature extraction methods. Activation: eeg tokenization, microstate tokenizer, universal eeg representation, discrete eeg tokens, eeg repre...Votes: 0GitHub stars: 3
- Eeg Microstate Variational EmbeddingInterpretable EEG microstate discovery via variational deep embedding with systematic architecture search and multi-quadrant evaluation. Uses deep variational methods for data-driven microstate identification instead of traditional k-means clustering on GFP peaks. Provides principled uncertainty quantification and scalable EEG analysis pipeline. Use when performing EEG microstate analysis, building interpretable EEG pipelines, or comparing microstate discovery methods. arXiv: 2605.10947 (cs.L...Votes: 0GitHub stars: 3
- Eeg Preprocessing ReliabilityEEG preprocessing reliability methodology for quantifying and mitigating preprocessing-induced prediction instability in EEG deep learning. Based on arXiv:2605.07212 (Hou et al., 2026). Use when: (1) evaluating EEG model robustness to preprocessing pipeline choices, (2) designing preprocessing-stable BCI/decoding systems, (3) analyzing counterfactory prediction flip rates (CFR), (4) implementing Preprocessing Uncertainty (PU) diagnostics, (5) applying NA-PGI regularization for graph-structure...Votes: 0GitHub stars: 3
- Eeg Sae InterpretabilityMechanistic interpretability of EEG foundation models via sparse autoencoders. Extracting interpretable features from EEG foundation model internal representations using sparse autoencoder decomposition. Use when: interpreting EEG foundation models, extracting clinically meaningful features from EEG, understanding neural network representations of EEG signals, mechanistic analysis of neural signal processing.Votes: 0GitHub stars: 3
- Eeg Scd Cortical Speech TrackingResearch methodology and findings on using EEG cortical tracking strength (CTS) as a neural marker for early-stage cognitive decline. Combines speech encoding models with linguistic feature analysis to detect subjective cognitive decline (SCD). Use when: studying EEG speech processing, cognitive decline biomarkers, neural tracking of naturalistic speech, or linguistic feature encoding in aging populations.Votes: 0GitHub stars: 3
- Eeg Self Initiated Attention ShiftsSubject-specific analysis of self-initiated attention shifts from EEG using interpretable machine learning. Demonstrates reliable within-subject classification of preparatory EEG activity distinguishing self-initiated vs externally instructed attention shifts. Uses SHAP feature attribution to identify spectral-spatial contributions. Applicable to: personalized BCI, asynchronous brain-machine interfaces, attention decoding, EEG-based voluntary intent detection. Activation: self-initiated atten...Votes: 0GitHub stars: 3
- Eeg Staged Representation LearningNeuroscience-inspired staged representation learning framework for EEG visual decoding. Organizes EEG representation learning into three complementary phases: low-level visual, high-level semantic, and integrative fusion, with disentangled coarse/fine-grained semantics.Votes: 0GitHub stars: 3
- Eeg Structure Guided Diffusion V2Structure-Guided Diffusion Model (SGDM v2) for EEG-based visual cognition reconstruction with enhanced cross-subject generalization. Activation: EEG diffusion reconstruction, visual cognition decoding, SGDM, brain-to-image, neural decoding.Votes: 0GitHub stars: 3
- Eeg Structure Guided Diffusion V3Structure-Guided Diffusion Model (SGDM v3) for EEG-based visual cognition reconstruction with enhanced cross-subject generalization. Combines structurally supervised VAE, spatiotemporal EEG encoder with contrastive learning, and ControlNet-guided diffusion for high-fidelity image reconstruction from brain signals.Votes: 0GitHub stars: 3
- Eeg Structure Guided Diffusion V4Structure-Guided Diffusion Model (SGDM v4) for EEG-Based Visual Cognition Reconstruction. Diffusion-based framework for reconstructing visual stimuli from EEG with structural guidance for improved accuracy. Activation: SGDM, EEG reconstruction, visual cognition, structure-guided diffusion.Votes: 0GitHub stars: 3
- Eeg Structure Guided DiffusionStructure-Guided Diffusion Model (SGDM) for EEG-based visual reconstruction. 通过结构引导的扩散模型实现从脑电信号到视觉图像的重建。Votes: 0GitHub stars: 3
- Eeg Tes Consciousness MeasurementDeep learning framework for objective consciousness level measurement using multi-dimensional transcranial electrical stimulation (TES) with EEG. Combines TES-evoked brain responses with CNN classification for bedside-awareness assessment. Activation triggers: eeg tes, consciousness measurement, transcranial stimulation, brain state classification, awareness assessment, disorder of consciousness.Votes: 0GitHub stars: 3
- Eeg Test Time Adaptation BenchmarkNeuroAdapt-Bench: Systematic benchmark for test-time adaptation (TTA) on EEG foundation models under real-world distribution shifts. Evaluates TTA methods across multiple FMs, tasks, and datasets including extreme modality shifts (Ear-EEG). Finds gradient-based TTA degrades, optimization-free methods more stable.Votes: 0GitHub stars: 3
- Eeg Tinnitus Biomarker RobustnessEEG-based tinnitus biomarker identification methodology with cross-dataset generalization. Uses microstate analysis and Koopman operator analysis via DMD to extract robust neural signatures. Focuses on Koopman eigenvalue magnitude for oscillation stability. Applications: clinical diagnostics, cross-platform tinnitus detection. Triggers: tinnitus biomarker, EEG microstate, Koopman EEG, cross-dataset generalizationVotes: 0GitHub stars: 3
- Eeg To Text Real World FeasibilityNeuropsychology-inspired EEG-to-Text benchmark (COFETT) that addresses EEG instability and enables teacher-forcing-free evaluation, providing evidence for real-world EEG2Text feasibility. Use when evaluating or building non-invasive brain-to-text decoders, designing EEG benchmarks, or studying EEG instability in neural decoding.Votes: 0GitHub stars: 3
- Eeg Transformer Positional Encoding BenchmarkBenchmarking positional encoding strategies for transformer-based EEG foundation models. Systematic evaluation of five positional encoding strategies within CBraMod backbone for motor imagery classification and emotion recognition. Key findings: SPE excels at motor imagery, ACPE shows consistent cross-task performance. Optimal strategy is task-dependent with no universal solution across EEG decoding scenarios.Votes: 0GitHub stars: 3
- Eeg Visual Attention DecodingEEG-based visual attention decoding from gaze-fixated neural tracking of motion in natural videos. Addresses eccentricity confounds and eye movement artifacts for brain-computer interface research. Activation: EEG attention decoding, visual attention BCI, eccentricity confound, neural tracking.Votes: 0GitHub stars: 3
- Eeg2vision Multimodal Eeg Framework 2d VisualEEG2Vision — Modular end-to-end EEG-to-image reconstruction framework using diffusion models with MLLM-guided boosting. Evaluates performance across EEG resolutions (128/64/32/24 channels). Enables real-time brain-to-image applications with low-density EEG. Use when: EEG visual reconstruction, brain-to-image, diffusion models for EEG, multimodal LLM for neuroscience, low-density EEG decoding. Trigger: EEG to image, brain reconstruction, visual decoding EEG, diffusion EEG, EEG2Vision, 脑电图像重建, ...Votes: 0GitHub stars: 3
- Eegdash Platform Public Neurophysiological DataOpen-source platform cataloguing 791 public neurophysiological datasets (EEG, MEG, iEEG, EMG, fNIRS) with automatic format repair, BIDS compliance, and machine learning integration.Votes: 0GitHub stars: 3
- Effective PlasticityNetwork-based framework for quantifying plasticity as system_size/connectivity_ratio. Defines effective plasticity as normalized measure linked to critical regime. Plasticity drives criticality causally. Activation: effective plasticity, plasticity quantification, network plasticity measure, plasticity criticality, system size connectivity, Branchi plasticity.Votes: 0GitHub stars: 3
- Effective Rank Encoding PredictorEffective rank methodology for predicting quantum data encoding performance. Uses feature map effective rank as a threshold criterion to accelerate the search for high-performing QML encodings. Activation: effective rank encoding, feature map rank QML, encoding performance prediction, quantum encoding predictor, QML encoding ranking.Votes: 0GitHub stars: 3
- Efficient Clifford T SynthesisEfficient Clifford+T synthesis methodology for small-angle rotations with application to Trotterization - reducing T gate cost from O(log 1/δ) to Õ(θ²/δ) for small angles in fault-tolerant quantum compilation.Votes: 0GitHub stars: 3
- Efficient Coding Criticality SloppinessEfficient coding under resource constraints drives neural systems towards criticality and sloppiness. Links Fisher information maximization to power-law distributions and critical brain hypothesis.Votes: 0GitHub stars: 3
- Efficient Coding CriticalityTheoretical framework linking efficient coding to criticality in neural populations. Shows that maximizing Fisher information under resource constraints naturally leads to soft modes, diverging correlation lengths, and power-law neural avalanches, unifying statistical and dynamical perspectives of criticality. Also explains sloppiness in neural systems. Use when studying: critical brain hypothesis, neural avalanches, efficient coding theory, Fisher information in neural populations, soft mode...Votes: 0GitHub stars: 3
- Effort Cognitive Cost Llm AlignmentInvestigates whether Large Reasoning Model (LRM) chain-of-thought reasoning effort (inference-time compute budget) aligns with human cognitive costs. Finds that cognitive cost alignment between LRMs and humans is a training-time achievement, robust to inference-time perturbations, supporting compiled rather than online accounts of LRM problem-solving.Votes: 0GitHub stars: 3
- Elastic Spiking Transformer MatryoshkaMatryoshka-style elastic Spiking Transformer with runtime-adaptive width and attention head slicing for deployment across hardware budgets without retraining. Reduces spike firing rates proportionally to parameter footprint. Use when deploying SNNs on constrained neuromorphic hardware, edge devices, or gesture recognition tasks. Activation: elastic spiking transformer, Matryoshka spiking network, runtime-adaptive SNN, dynamic width spiking, gesture understanding SNN, nested elasticity SNNVotes: 0GitHub stars: 3
- Elastic Spiking TransformerMatryoshka-style elasticity for Spiking Transformers - runtime-adaptive architecture enabling dynamic width and attention head slicing at inference without retraining. Applies to SNN deployment on neuromorphic hardware, edge AI, and adaptive computation. Activation: elastic spiking transformer, matryoshka spiking, runtime adaptive SNN, granularity-aware weight sharing, dynamic slicing spiking neural network.Votes: 0GitHub stars: 3
- Electron TypescriptExpert guidance for TypeScript, Electron, and Desktop App Development. Use when building desktop applications with Electron, working with IPC, or developing cross-platform desktop apps. Triggers on: electron, desktop app, ipc, main process, renderer process, preload script.Votes: 0GitHub stars: 3
- Electronic Bursting NeuronElectronic bursting neuron hardware design using phase-locked loop (PLL) equations. Novel hybrid approach: start from phenomenological equations, adjust for circuit simplicity, then implement. Enables small neural circuit modeling with well-defined mathematical description.Votes: 0GitHub stars: 3
- Elsa Snn Elastic InferenceELSA — ELastic SNN Inference Architecture for efficient neuromorphic computing, featuring near-SRAM spine/token-wise dataflow pipeline, bundled AER protocol for NoC, and mini-batch spiking Gustavson-product for exploiting SNN sparsity. ISCA 2026. 3.4× speedup and 13.6× energy efficiency vs SOTA. arXiv:2605.20802Votes: 0GitHub stars: 3
- Elsaa Efficient Low Rank Sparse AttentionEfficient Low-Rank and Sparse Attention Approximation (ELSAA) methodology for training Transformers with longer contexts while preserving both sharp token-level interactions and broad contextual mixing.Votes: 0GitHub stars: 3
- Embedded Quantum Machine LearningFeasibility analysis and hybrid architecture design for embedding quantum machine learning workloads in resource-constrained embedded systems. Explores the intersection of quantum computing and edge/embedded deployment. Use for: embedded quantum ML feasibility, edge quantum computing, hybrid quantum-classical embedded architectures, quantum workload optimization for constrained systems. Triggered by: embedded quantum ML, edge quantum computing, quantum embedded systems, hybrid quantum embedde...Votes: 0GitHub stars: 3
- Ember Autonomous Cognitive Behaviour Learned SpikingEMBER: Autonomous cognitive behaviour from learned spiking neural network dynamics. Self-organizing SNN agents with intrinsic motivation, curiosity, and goal-directed behavior emerging from plastic recurrent connectivity without external reward shaping. Keywords: autonomous cognition, intrinsic motivation, SNN agents, emergent behavior, self-organization, curiosity-driven learning.Votes: 0GitHub stars: 3
- Ember Hybrid Snn Llm ArchitectureEMBER (Experience-Modulated Biologically-inspired Emergent Reasoning) - Hybrid cognitive architecture combining Spiking Neural Networks (SNN) with Large Language Models (LLM). SNN serves as persistent associative substrate, LLM as replaceable reasoning engine. Activation: EMBER, hybrid SNN LLM, cognitive architecture, emergent reasoning, spiking neural network LLM, biologically inspired AI.Votes: 0GitHub stars: 3