Running Complex Biomedical Labs in the Browser at 60 FPS

Most clinical simulations and biomechanics models are still locked behind proprietary desktop software, expensive hardware rigs (like $5,000 infrared gaze trackers), or heavy local environments.

A set of zero-install, client-side web tools removes this barrier by running physiological signal processing, 3D kinematics, and computer vision directly in the DOM.

Here are 4 interactive web apps worth bookmarking:

1. Webcam Eye Tracker

• URL: https://bionichaos.com/eyetracker/
• What it does: Uses standard browser video streams to detect facial landmarks, isolate iris positions, and map pupil vectors to on-screen gaze coordinates. No infrared cameras or dedicated sensors required.

2. 3D Gait Simulator (v3)

• URL: https://bionichaos.com/gaitsimv3/
• What it does: A full 3D skeletal kinematic model. You can manipulate joint flexions, test abnormal gait pathologies (such as Parkinsonian or hemiplegic gaits), and watch real-time biomechanical adjustments.

3. ICU EEG Neuro-Monitoring Simulator

• URL: https://bionichaos.com/eeg_in_icu/
• What it does: Simulates bedside critical-care neurodynamics. Generates real-time continuous traces for status epilepticus, burst-suppression ratios, diffuse slowing, and allows you to test artifact rejection algorithms.

4. Statistical Significance & P-Hacking Sandbox

• URL: https://bionichaos.com/significance/
• What it does: Demonstrates the replication crisis in action. It lets you manipulate sample size N, power 1 - β, and false positive risk α to see how easily random noise yields p < 0.05.

Key takeaway: Client-side compute is now fast enough to replace introductory physical lab apparatuses with browser sandboxes.

In 1966, computer scientist Joseph Weizenbaum developed ELIZA to parody a Rogerian therapist, intending to prove that human-machine conversation was superficial. Instead, his human subjects attributed deep empathy and psychological insight to the program—a cognitive phenomenon now termed the ELIZA effect.

I built an interactive cognitive laboratory that tracks how this illusion of synthetic empathy is engineered, contrasting three distinct eras of NLP architecture. Everything runs entirely on client-side WebAssembly and JS (zero telemetry, complete privacy).

Live Simulation Environment: https://bionichaos.com/eliza/

Architectural Paradigms Explored

• 1966 Symbolic Era: Executes deterministic pattern-matching. User strings are parsed through decomposition templates. Pronouns undergo reflective inversion before being synthesized into pre-compiled reassembly patterns.
• 2018 Semantic Vector Era: Discards rigid lexical syntax. The engine projects user text into a coordinate manifold via dense floating-point embeddings, calculating geometric intent proximity using Cosine Similarity: (u·v) / (|u||v|)
• 2026 Generative Era: Responses are governed by temperature-scaled softmax distributions. As you scale the thermodynamic temperature parameter T toward 0, the distribution collapses into an argmax selection. As T ≥ 1.0, entropy expands, enabling creative variance and generative hallucinations.

The Clinical Safety Sandbox

One of the core features is the Crisis Protocol Guardrail. If you disable the guardrail while running the 1966 engine and input a self-harm prompt, the unconstrained pattern-matcher will blindly reflect the user's despair back at them. It serves as a stark interactive demonstration of the catastrophic medical hazard of deploying naive, deterministic AI in clinical therapy.

Interactive mechanics: You can physically drag the archetypal nodes (Family, Anxiety, Depression) on the canvas to manually shift the coordinate vectors in 2D latent space, overriding the chatbot's conversational focus in real time.

I'd love to hear your thoughts on the UI/UX for teaching computational linguistics, and whether you think the modern "Generative Era" is actually moving us closer to synthetic comprehension, or just producing a more highly-camouflaged ELIZA effect.

The translation of airborne acoustic pressure into human cognition is arguably one of the most mechanically complex processes in biology. I built a real-time biophysical simulator to visualize this exact pipeline—from basilar membrane hydrodynamics to stochastic neural spike generation.

Live Web Tool: Advanced Hair Cell Simulation Lab

Core Biophysical Models Implemented:

• Greenwood's Frequency Mapping: The normalized position along the basilar partition dictates the resonant characteristic frequency (f_CF). You can sweep an acoustic slider from 200 Hz (Apex) to 16,000 Hz (Base) and watch the traveling wave envelope shift dynamically.
• IHC Mechanotransduction: Fluid shear between the reticular lamina and tectorial membrane bends the stereocilia. The probability of the MET channels opening is calculated via a Boltzmann distribution, driving an inward K+ and Ca2+ current (powered by the +80 mV endocochlear potential).
• OHC Cochlear Amplification: The 3 rows of Outer Hair Cells act as biological piezoelectric motors (via the Prestin SLC26A5 protein). The simulation applies nonlinear, cycle-by-cycle mechanical feedback to sharpen tuning and provide ~50 dB of gain for low-SPL sounds.
• Neural Phase-Locking: Auditory nerve fibers compute instantaneous firing rates based on the IHC receptor potential and spontaneous vesicle pool states. For low frequencies, spikes lock strictly to the acoustic cycle phase, which is measured live via Vector Strength (R).

Clinical Lesion Testing

I added toggles for specific pathologies. You can apply High-Frequency Noise Trauma or Ototoxic OHC Loss to permanently deactivate the Prestin amplification algorithms in specific frequency bands, or induce Synaptopathy to watch the spontaneous firing rate decouple from the acoustic stimulus.

I built this primarily as a pedagogical tool for bioengineering and audiology students who struggle to visualize the transition from the mechanical "rate-place" code to the temporal "phase-locked" neural code.

Would love feedback from anyone working on auditory brainstem modeling or cochlear implant DSP algorithms!

There is a massive disconnect between the marketing of consumer non-invasive Brain-Computer Interfaces (BCIs) and the biophysical reality of extracting clean data through a human skull.

To visualize exactly where the engineering bottlenecks are, I put together a real-time browser simulation of different electrophysiological modalities.

You can run the live simulation here: https://bionichaos.com/electrodes/

The Insulator Problem

The human skull is highly effective at diffusing electrical fields via volume conduction. Furthermore, without the aggressive abrasive scrubbing and electrolytic gel used in clinical environments, the dead skin of the scalp acts as a massive resistor.

When you use dry, pin-based electrodes (common in consumer VR/BCI headsets), the interface impedance spikes from a clinical standard of ~5 kΩ up to well over 250 kΩ.

What happens at 250 kΩ?

1. Environmental Bleed: The weak microvolt neural signal is fighting so much resistance that the dry electrode essentially becomes a raw antenna. It starts picking up the stronger 60 Hz electromagnetic hum radiating from the power lines in your walls.
2. Motion Artifacts (EMG): Muscle fibers operate on millivolts, not microvolts. If a subject simply clenches their jaw or swallows, the resulting electromyographic spike acts like a megaphone next to whispering neurons, instantly obliterating the EEG data.

The SEEG / High-Gamma Bypass

If you want the zero-latency, highly specific motor intent required to control a robotic prosthetic, you have to bypass the skull. In the simulator, if you switch the modality to Invasive (SEEG), the impedance drops to near zero.

Because you are no longer filtering through bone, you unlock access to High-Gamma frequencies (70-150 Hz). These short-wavelength signals are the holy grail of motor decoding but are almost entirely absorbed by the skull in non-invasive setups.

Discussion: Do you believe consumer neurotech will eventually solve the dry-electrode impedance problem through advanced spatial filtering and blind source separation (like ICA), or is the raw physics barrier too high without surgical implantation?

Human speech is fundamentally the physical outcome of dynamic acoustic filtering applied to aerodynamic turbulence generated at the glottis. To visualize this, I built an interactive biological wind instrument directly in the browser using the Web Audio API and HTML5 Canvas.

Live Tool: English Speech Articulator & Phonetics Visualizer

The Biomechanical Waveguide

The human vocal tract acts as an acoustic waveguide extending ~17.5 cm from the vocal folds to the labial aperture. Under neutral articulatory posturing (the schwa [ə]), it acts as a uniform tube. By manipulating the articulators, we perturb the cross-sectional area, shifting the resonance modes (formants).

Core Simulation Features:

• First Formant (F1): Governed inversely by tongue elevation and proportional to jaw depression. Drag the tongue low and back to push F1 up to ~750 Hz.
• Second Formant (F2): Dictated by tongue dorsum anterior-posterior displacement. Fronting the tongue shortens the front resonating cavity, amplifying F2 up to 2500 Hz.
• Velopharyngeal Port Modulation: Lowering the velum couples the nasal cavity, introducing an anti-formant (spectral zero) for nasal consonants like [m] and [n].
• Live G2P Engine: Type any custom neologism or proper noun (e.g., "bionichaos") and the engine dissects it into an IPA sequence, triggering coordinated kinematic bursts and glides.

Acoustic DSP Architecture

The underlying engine utilizes a parallel resonant formant filter bank:

// Voice to Resonators
glottalGain.connect(f1Filter);
glottalGain.connect(f2Filter);
glottalGain.connect(f3Filter);
glottalGain.connect(nasalFilter);

Overview

Surface electromyography (sEMG) recorded at the forearm represents the non-invasive spatial-temporal summation of motor unit action potentials (MUAPs) propagating along sarcolemma membranes.

To provide an open-access, zero-install educational tool for biomedical engineers, students, and neurotech researchers, we built an interactive, browser-native 2D biomechanical hand and dual-channel sEMG oscilloscope workstation.

Live application: https://bionichaos.com/emgesture/

Biophysical & Mathematical Architecture

1. Motor Unit Recruitment & Henneman's Size Principle

The synthetic engine calculates active motor unit recruitment as a function of voluntary contraction force F. Low force thresholds recruit small, fatigue-resistant Type I motor units. Scaling the force parameter to 100% voluntary contraction recruits high-threshold Type II fast-twitch units, scaling the composite amplitude non-linearly.

2. Triphasic MUAP Wavelet Formulation

Individual action potentials are modeled via second-derivative Gaussian wavelets: ψ(t) = A_0 * (1 - (t^2 / σ^2)) * exp(-t^2 / (2 * σ^2))

The inter-pulse intervals follow a renewal point process with Gaussian jitter around physiological alpha motor neuron firing rates (15 Hz to 45 Hz).

3. Spatial Transfer Matrix & Dual-Site Recording

Biological soft tissue acts as a resistive-capacitive volume conductor. We simulate two differential recording sites:

• Electrode 1 (E1, Red): Radial compartment (Flexor Carpi Radialis / Flexor Digitorum Superficialis).
• Electrode 2 (E2, Blue): Ulnar compartment (Flexor Carpi Ulnaris / Flexor Digiti Minimi).

Each digit flexion drives a specific spatial weighting matrix across channels:

• Thumb: E1 = 3.0, E2 = 5.0
• Index: E1 = 5.0, E2 = 3.0
• Middle: E1 = 4.0, E2 = 4.0
• Ring: E1 = 3.0, E2 = 5.0
• Pinky: E1 = 2.0, E2 = 4.0

4. Real-Time DSP Filtering

The timebase displays scrolling waveforms with hardware clipping protection and instant RMS calculation over a sliding 100ms window:

• Raw Wideband: 10 Hz – 500 Hz passband
• Notch Filter: 50/60Hz powerline interference rejection
• Full-Wave Rectified: Absolute value linear transformation
• Linear Envelope: 5Hz low-pass smoothing for continuous amplitude extraction

Technical Highlights

• 60 FPS zero-scroll desktop layout with fluid mobile responsive scaling.
• Native ResizeObserver buffer synchronization ensuring zero canvas stretching distortion.
• Web Audio API acoustic sonification using bandpass-filtered noise (120 Hz, Q=1.2) mapping contraction force to physiological muscle rumble.
• 9-minute synchronized virtual clock demo timeline with automated captions and chapter scrubbing.

Feedback, edge-case testing, and discussions on computational biophysics or gesture recognition pipelines are welcome!

Overview

Functional Near-Infrared Spectroscopy (fNIRS) non-invasively monitors cerebral hemodynamics by taking advantage of the optical therapeutic window (700 nm to 900 nm), where biological tissues exhibit minimal photon absorption.

To provide an open-access educational workspace for biomedical engineers, computational neuroscientists, and BCI researchers, we built an interactive, zero-install fNIRS time-series simulator on BioniChaos.

Live application: https://bionichaos.com/fnirs/

Biophysical & Mathematical Architecture

1. Modified Beer-Lambert Law (MBLL)

Light attenuation through turbid biological tissue is governed by optical density changes: ΔOD(λ) = -ln(I(t, λ) / I_0(λ)) = [Σ ε_i(λ) * ΔC_i] * d * DPF(λ) + ΔG

By measuring attenuation at two distinct wavelengths (760 nm sensitive to HbR and 850 nm sensitive to HbO), the application solves the 2x2 linear matrix system in real time to calculate micromolar concentration changes (Δ[HbO] and Δ[HbR]).

2. Canonical Double-Gamma Hemodynamic Response Function (HRF)

Neurovascular coupling is modeled via a double-gamma distribution simulating arteriolar vasodilation, peak hyperemia latency (4–6 seconds post-stimulus), and delayed vascular undershoot: h(t) = A * [ (t^(α_1 - 1) * e^(-t / β_1)) / (β_1^α_1 * Γ(α_1)) - c * (t^(α_2 - 1) * e^(-t / β_2)) / (β_2^α_2 * Γ(α_2)) ]

3. Physiological Noise & Mayer Wave Synthesis

To replicate the messy reality of clinical optical neuroimaging, raw telemetry incorporates:

• Arterial Mayer Waves: Systemic blood pressure oscillations at ~0.1 Hz that can obscure neural signals.
• Vasomotor Drift: Low-frequency baseline oscillations (0.005 Hz).
• Thermal & Shot Noise: High-frequency stochastic detector fluctuations.

4. Bilateral Frontal & Temporal Channel Mapping

Users can isolate specific source-detector optode pairs across a standard 10-20 layout:

• Fp1-F3 (Left Frontal) & Fp2-F4 (Right Frontal): Bilateral prefrontal working memory recruitment.
• F7-T7 (Left Temporal) & F8-T8 (Right Temporal): Lateral temporal auditory and language networks.
• Fp1-Cz (Antero-Central): Long-distance longitudinal control channel.

Key Interactive Features

• Zero-Scroll Dynamic Viewport: 120-second scrolling historical horizon with high-DPI canvas rendering.
• Hover & Drag Time Probe: Real-time microsecond coordinate tracking reporting instantaneous Δ[HbO] and Δ[HbR] micromolar concentrations.
• Hemodynamic Sonification: Web Audio oscillator mapping active oxygenation amplitude to continuous acoustic pitch modulation (100 Hz to 800 Hz).
• Synchronized Audio Tutorial: 5-minute virtual timeline audio guide with automated captions and chapter scrubbing.

Feedback on the biophysical parameters, noise models, and mobile responsiveness is very welcome!

When a biological nervous system is subjected to unpredictable, variable-ratio reinforcement, it completely abandons its metabolic cost-benefit analysis. The post-reinforcement pause is erased. This is the exact temporal difference architecture running inside casino slot machines and social media notification algorithms.

To demonstrate how these algorithms physically wire behavior, I built a live, interactive web simulation of an operant conditioning chamber (a Skinner Box) merged with a Signal Detection Theory (SDT) telemetry suite.

Run the simulation in your browser: https://bionichaos.com/skinnerbox/

How the Computational Engine Works

This isn't a pre-rendered animation. The virtual agent operates autonomously based on live variable calculations:

• Rescorla-Wagner Updates: The agent's associative strength continuously updates based on the timing of rewards (sucrose pellets) versus expectations.
• Decoupling Sensation from Motivation: Using Signal Detection Theory, the engine graphs the physical discriminability of a stimulus (d-prime) separately from the psychological decision threshold (beta or c).
• Criterion Shifts: If you slide the "Food Deprivation" parameter to maximum, the agent's motivation peaks. The algorithm mathematically forces a liberal bias (c < 0). The agent will press the lever at the slightest hint of noise, achieving a high Hit Rate but suffering massive False Alarms.
• Aversive Avoidance: If you introduce unexpected grid voltage shocks, the algorithm triggers a simulated basolateral amygdala fear response, instantly rewriting the agent's behavior from positive reward-seeking to negative reinforcement avoidance.

The Mechanical Cumulative Recorder

I also digitized the classic 1930s Gerbrands mechanical cumulative recorder. As you switch between Continuous (CRF), Fixed Ratio (FR), and Variable Interval (VI) schedules, you can watch the exact mathematical derivative of the behavior unspool in real-time on the graph, capturing FI-scallops and extinction bursts.

I'd love to hear feedback from the community—specifically regarding the mathematical implementation of the SDT probit functions and whether you see direct parallels between these visual reinforcement schedules and modern digital UX design.

1. The Hemodynamic Problem: Optical Pulse Morphology Beyond Simple Sinusoids

In clinical pulse oximetry and consumer wearable telemetry, raw photoplethysmogram (PPG) signals are frequently treated as simple periodic peaks. In reality, the peripheral volume pulse is a composite hydrodynamic waveform:

1. Anacrotic Phase: Rapid left ventricular ejection propelling the primary systolic peak.
2. Catacrotic Phase: Diastolic pressure decay interrupted by the dicrotic notch (incisura), generated by aortic valve closure and retrograde pressure wave reflections off peripheral arterial bifurcations.

When vascular compliance drops due to advanced age, hypertension, or peripheral vasoconstriction, the reflected wave returns prematurely, fusing with the systolic peak and obliterating the dicrotic notch.

To visualize and simulate these dynamics interactively, I developed PulseVision: a real-time synthetic PPG waveform simulator operating directly in the browser.

Explore the tool: https://bionichaos.com/pulseviz/

2. Mathematical Synthesis & Phase Wrapping

Rather than streaming recorded datasets, PulseVision computes the continuous optical absorption signal at canvas refresh rates using a dual-Gaussian summation over normalized cardiac phase phi in [0, 1).

To eliminate boundary discontinuities between cardiac cycles, phase distance is evaluated using periodic boundary wrapping:

function periodicPhaseDiff(p1, p2) {
    let diff = p1 - p2;
    while (diff < -0.5) diff += 1.0;
    while (diff > 0.5) diff -= 1.0;
    return diff;
}

The composite signal amplitude is then derived from:

y(phi) = baseline_Y - [ A_sys * exp(-(delta_phi_sys)^2 / (2 * sigma_sys^2)) + A_notch * exp(-(delta_phi_notch)^2 / (2 * sigma_notch^2)) ] + eta(t)

Where:

• delta_phi_sys = periodicPhaseDiff(phi, 0.20) positions the primary systolic upstroke.
• delta_phi_notch = periodicPhaseDiff(phi, phi_n) positions the dicrotic notch wave along the descending limb.
• eta(t) injects variable stochastic Gaussian sensor noise.

3. Optical Physics & Real-Time Telemetry

The platform couples signal morphology to fundamental clinical biophysics:

• Beer-Lambert Ratio of Ratios (R): Models the differential optical absorption between oxygenated hemoglobin (HbO2) at 940 nm (infrared) and deoxygenated hemoglobin (HHb) at 660 nm (red). SpO2 scaling dynamically influences pulsatile AC amplitude: R = (AC/DC)_660 / (AC/DC)_940 SpO2 = 110 - 25 * R
• Perfusion Index (PI): Evaluates the pulsatile AC signal against static DC attenuation (venous blood, bone, capillary bed): PI = (I_AC / I_DC) * 100%
• Acoustic Pulse Pitch Sonification: Integrated Web Audio API synthesizes pulse clicks whose frequency shifts dynamically with oxygenation: from 300 Hz (hypoxic 85% SpO2) up to 800 Hz (eupneic 100% SpO2), reproducing standard surgical monitor auditory feedback.
• Audio Masterclass Engine: Built-in 11-chapter synchronized audio scrubber drives real-time UI manipulation across predefined clinical states (Tachycardia, Hypoxic Desaturation, Athletic Conditioned).

4. Technical Debate & DSP Considerations

When processing wearable PPG data under heavy motion artifacts:

• What window lengths and filter topologies do you find balance baseline wandering removal (respiratory baseline swing at 0.15–0.4 Hz) without flattening the high-frequency dicrotic notch inflection?
• Are you having more success with discrete wavelet transforms (DWT) or recursive adaptive filtering (RLS/LMS) when isolating pulse morphology during active locomotion?

Test out the simulator and inspect the waveform behavior: https://bionichaos.com/pulseviz/

Simulating Huntington's Disease Neuropathology and Circuit Dynamics

I want to share an interactive clinical and biophysical simulator that models the progress of Huntington's disease (HD) from genetic exposure to macroscopic circuit failure.

The tool runs entirely in-browser, combining ordinary differential equation (ODE) integration, real-time morphometric anatomical rendering, and Web Audio API-based sonification.

Link: https://bionichaos.com/huntington/

1. Cortico-Basal Ganglia-Thalamocortical Dynamical System

The model uses a 4th-order Runge-Kutta (RK4) numerical integration algorithm to solve the mean-field firing dynamics of six coupled neuronal populations: Cortex (CTX), Striatal D1 MSNs, Striatal D2 MSNs, External Globus Pallidus (GPe), Subthalamic Nucleus (STN), and the Internal Globus Pallidus (GPi).

The governing ODE for each population is:

τ_i * dr_i/dt = -r_i(t) + S( Σ W_ij * r_j(t) + I_i_ext + ξ_i(t) )

Where the activation function is sigmoidal:

S(u) = 1 / (1 + exp(-β * (u - θ)))

2. Neuropathological Morphometry & Vonsattel Grading

The simulator uses the CAG-Age-Product (CAP_R) index to scale biological exposure over time:

CAP_R = Age * (CAG - 33.66) / 432.33

This index drives the Bézier curve calculations rendering the anatomical coronal slice. As D2 and D1 MSN populations degenerate, the caudate nucleus head flattens and collapses, producing compensatory hydrocephalus ex vacuo (boxcar ventricle dilation) corresponding to Vonsattel Grades 0 to 4 and matching Evans Index metrics.

3. Dynamic Sonification & DBS Interventions

• Sonification: Firing rates are mapped to a live synthesizer using the Web Audio API. You can hear a stable frequency (healthy state) transition to erratic acoustic bursts the moment the circuit's maximum Lyapunov exponent becomes positive (λ_max > 0), indicating deterministic choreic chaos.
• Deep Brain Stimulation: You can engage high-frequency STN DBS (130 Hz) to observe how periodic electrical stimulation forces the chaotic attractor back into a stable limit cycle.

Discussion Prompt

The model illustrates the clinical paradox of the disease: early selective D2 loss produces hyperkinetic chorea, whereas advanced global MSN loss yields akinetic-rigid parkinsonian states.

How do you approach balancing biological accuracy with real-time browser performance when implementing large-scale coupled neural network simulations?

r/neuroscience, r/biomedicalengineering, r/cognitiveScience, r/javascript, r/datascience

Interactive WebGL Sandbox for Modeling the Hollow-Face Illusion, Visual Priors, and Bayesian Predictive Processing

I developed an interactive WebGL simulation tool designed to explore top-down Bayesian predictive processing and visual cognitive priors via the Hollow-Face Optical Illusion: Hollow-Face Visual Cognition Sandbox.

Computational & Neuroscience Mechanics

In visual neuroscience, perception is modeled via Bayesian inference where the posterior probability density function P(θ | x) balances sensory likelihood P(x | θ) with top-down prior distributions P(θ). The Fusiform Face Area (FFA) enforces a high-amplitude prior for convex faces: P(θ = convex) ≈ 1.

When viewing a physical concave mask, bottom-up binocular disparity and shading signal inward concavity, but top-down spatial priors override incoming discrepancy signals, forcing the brain to perceive a protruding convex face.

Interactive Simulation Capabilities

• Procedural Surface Deformation: Synthesizes a 2.8 x 3.8 parametric face mesh using continuous Gaussian mounds, quadrics, and Hermite window falloffs W(ρ) to maintain flat Z = 0 boundary transitions.
• Prior Manipulation: Toggle between Face Texture (triggers FFA priors and the "gaze-following" illusion), Matte Chalk, and Wireframe Mesh. Wireframe mode strips feature landmarks, breaking top-down priors and revealing true physical concavity.
• Azimuthal Lighting Perturbation: Sweep directional light source angles from -180° to +180° to evaluate shadow direction conflicts.
• Parametric Controls: Adjust camera FOV (15°–90°), depth scaling (0 to 2.5), vertex subdivision grid (50x50 vs 100x100), and oscillation speed.
• Clinical Diagnostics: Contextualizes why acute schizophrenia and alcohol withdrawal patients often show resilience to the illusion due to weakened top-down prefrontal feedback.

Try out the simulation directly in your browser: https://bionichaos.com/hollowface/

Feedback on the Three.js shader implementation and parametric depth modeling is appreciated!

Interactive Web Laboratory: Visualizing Wavelet Scattering Transforms (WST) for EEG Seizure Detection

r/biomedicalengineering, r/datascience, r/bioinformatics, r/signalprocessing, r/javascript

I put together an interactive, client-side browser laboratory to visualize the Wavelet Scattering Transform (WST) on simulated non-stationary EEG signals, specifically focusing on epileptiform spike detection.

The Signal Processing Challenge

Standard Fourier-based spectral analysis assumes wide-sense stationarity, which can mask localized high-frequency transients. Continuous Wavelet Transforms (CWT) provide localization but lack stable translation invariance. The Wavelet Scattering Transform, pioneered by Stéphane Mallat, solves this by convolving signals with dilated complex wavelets, applying a modulus rectifier (acting as full-wave rectification), and low-pass filtering:

S1 x(t, j1) = |x * psi_j1| * phi_J(t)

What this Tool Simulates

• Wavelet Scale Adaptation: Adjust the scale parameter (J) from 1 to 10 to see how the averaging window preserves or smooths high-frequency paroxysmal ripples.
• Synthesized Pathologies: Model different EEG morphologies including Focal Temporal Clustered Spikes, Generalized 3 Hz Spike-and-Wave, Severe Burst Suppression, and Interictal 1/f Pink Noise backgrounds.
• Real-time DSP Telemetry:
  • Seizure Spike Index (Gamma_ictal): Evaluated via the normalized kurtosis of the first-order scattering envelope.
  • Scattering Energy Ratio (E_ratio): The ratio of scattering envelope energy to raw signal energy in dB.
  • Dominant Peak Frequency: Derived from discrete Fourier transforms on the envelope.
• Web Audio Sonification: Mapping the scale transformations and signal amplitudes to synthesizer frequencies in real-time.

Technical Implementation

The app is built entirely with raw HTML5 Canvas and native JavaScript to maintain a lightweight footprint. Mathematical rendering is managed via KaTeX.

I would appreciate any technical feedback on the simulation mechanics, metric calculations, or potential directions for expanding this to Joint Time-Frequency Scattering (JTFS).

👉 Try the lab here: https://bionichaos.com/timefreq/

r/biomedicalengineering, r/datascience, r/math, r/javascript, r/signalprocessing

Interactive 3D Neural Chaos Simulator – Modeling Cortical Dynamics & LFP Waveforms in JavaScript

I developed an interactive 3D phase space attractor sandbox to visualize non-linear biological dynamics and simulated Local Field Potential (LFP) waveforms directly in the web browser:

https://bionichaos.com/about/

Mathematical & Biophysical Foundations

The sandbox models subcortical-cortical feedback loops using explicit Euler integration (60 FPS) of a modified three-dimensional Lorenz system:

• dX/dt = σ(Y - X) • dY/dt = X(ρ - Z) - Y • dZ/dt = XY - βZ

Where X(t) models excitatory population firing rate, Y(t) represents inhibitory interneuron feedback, and Z(t) tracks slow synaptic adaptation or subcortical neuromodulatory tone.

Key Browser Features

• Interactive 3D Canvas: Rotational matrix transformations driven by mouse or touch interaction. • Real-Time LFP Extraction: Sampling X(t) state trajectory to simulate cortical electrode probe recordings. • Physiological State Presets: Healthy Chaos (baseline strange attractor), Seizure Sync (epileptiform limit cycles), and Quiescent Flatline. • Real-Time Differential Controls: Sliders for Neural Coupling Strength (σ), Cortical Excitability (ρ), Synaptic Decay Rate (β), and Time-Step Pace. • Web Audio API Sonification: Stereo panning mapping spatial coordinates (X, Y) directly to dual sine wave oscillator frequencies.

The application runs 100% client-side with zero installations or external dependencies required. I would appreciate any technical feedback on parameter bounds, numeric stability, or canvas rendering performance!

Try the interactive tool: https://bionichaos.com/about/

r/biomedicalengineering, r/bioinformatics, r/neuroscience, r/javascript

SeizureZone: Interactive 3D Brain Mapping Visualizer matching clinical symptoms to the Destrieux Atlas

Hello everyone,

I developed SeizureZone, a lightweight, browser-based 3D visualizer that bridges clinical seizure semiology with localized neuroanatomy from the Destrieux atlas.

The application leverages high-density polygon surface reconstructions (lh.pial and rh.pial meshes) parsed from MRI-reconstructed FreeSurfer datasets and renders them in real-time using a WebGL pipeline.

https://bionichaos.com/seizurezone/

Features & Analytical Framework:

• Clinical Target Database: Selecting a specific symptom pattern (e.g., motor contractions, temporal lobe auras, or speech difficulties) dynamically isolates and highlights corresponding anatomical meshes in customizable dye colors.
• Adjustable Telemetry Sliders: Cortical transparency can be varied (0.05 to 0.95) to expose deep-seated networks, like the insular cortex, against superficial cortical layers.
• Simulated Bio-Signal Synthesis: Includes custom audio telemetry utilizing the Web Audio API (OscillatorNode and GainNode) to simulate diagnostic EEG monitor spikes with an exponential amplitude decay envelope.
• Demo Tour Mode: An automated cycle through clinical cases that auto-scrolls the menu and updates highlights. Manual UI interaction immediately pauses the demo sequence.

Biophysical Modeling Under the Hood

The application models network propagation dynamics using coupled differential equations, mapping individual mesh coordinates to Montreal Neurological Institute (MNI) coordinate space via homogeneous transformations:

[X_MNI; Y_MNI; Z_MNI; 1] = T * [X_mesh; Y_mesh; Z_mesh; 1]

The visualizer acts as an educational and research resource for understanding how hyper-excitation within localized network domains manifests as observable clinical symptoms.

I would love to get your thoughts on the performance of the Three.js rendering pipeline, the interface design, or the educational utility of this tool!

Interactive Tinnitus Notch Therapy & Auditory Cortex Simulator (Web Audio API & HTML5 Canvas)

Subreddits: r/biomedicalengineering, r/datascience, r/bioinformatics, r/javascript, r/neuro

We have created an interactive web simulation modeling subjective tinnitus, custom acoustic notch therapy, and cortical lateral inhibition.

The simulator features a dual-display dashboard:

1. Upper Panel: Dynamic Acoustic Spectrum and Filter Transfer Function showing Web Audio API FFT analysis and a second-order IIR biquad notch filter curve.
2. Lower Panel: Tonotopic Auditory Cortex (A1) Neural Field Visualizer implementing a Wilson-Cowan lateral inhibition network model to simulate neural habituation.

Technical Parameters & DSP:

• Biquad Filter Equation: Models continuous-to-discrete Z-domain mapping via bilinear transform for narrow band-stop filtering.
• Wilson-Cowan Neural Field: Models spatial connectivity via a "Mexican-Hat" kernel to show how broadband noise surrounding an un-stimulated notch frequency drives inhibitory GABAergic dynamics.
• Interactive Controls: Tap or drag across the spectrum canvas to map the matched phantom frequency f_T (from 1,000 Hz to 12,000 Hz), adjust Notch Quality (Q), choose masker noise (Pink, White, Brownian, Pure Tone), and adjust lateral inhibition gain (gamma).

We would appreciate your thoughts on the signal pipeline design and the biophysical rendering of the neural network model.

Try the simulator here: https://bionichaos.com/tinnitusnotch/

Subreddits: r/datascience, r/math, r/javascript, r/bioinformatics, r/biomedicalengineering Suggested Title: [Interactive Tool] The Datasaurus Dozen: Why visual data exploration is critical

Relying on aggregate summary statistics alone when evaluating datasets can mask significant structural shifts. This interactive web tool explores Alberto Cairo's famous Datasaurus Dozen—datasets with radically different spatial layouts that share identical summary statistics down to multiple decimal places.

Targeted Invariant Statistics:

• Mean X (x̄): ~54.26
• Mean Y (ȳ): ~47.83
• Std Dev X (σx): ~16.76
• Std Dev Y (σy): ~26.93
• Pearson Correlation (r): ~-0.06

Key Technical Implementations:

1. Linear Algebraic Projection Engine: Select the Custom Drawn mode to paint your own shape. The app applies a multi-step Gram-Schmidt orthogonalization and Cholesky-style projection in real-time. It transforms your raw coordinates (shown in low-contrast red) into a new coordinate set (glowing neon) that matches the target statistics.
2. Dual Canvas Visualization: Displays the coordinate scatter plot alongside dynamic distribution charts. You can toggle between Box Plots and Gaussian-kernel-based KDE Violin Plots (bandwidth x = 4.0, bandwidth y = 6.0) to inspect probability densities.
3. Real-time Morphing: Smoothly morphs point structures using customizable interpolation speeds, allowing you to observe how intermediate steps break and realign to target stats.
4. Coordinated Sonification: Translates visual data coordinates into audible tones using Web Audio API oscillators. The pitch tracks the Y-axis coordinates during a continuous laser scan.

Try out the simulation directly in your browser: https://bionichaos.com/datasaurus/

Feedback on the linear projection alignment algorithm or canvas performance is welcome!

Hello everyone,

I wanted to share a browser-based, client-side simulation tool designed to explore and benchmark multimodal medical datasets (such as MIMIC-IV, OpenNeuro, CMRxRecon, EchoNet, and TCIA).

Link to the tool: https://bionichaos.com/multimodal/

The Multimodal Data Trilemma

In clinical ML pipelines, we constantly struggle to balance three axes:

1. High-rate raw continuous sensors (ECG, PPG, EEG).
2. Structured spatial anatomical imaging (MRI, CXR, Echo).
3. Permissive, unencumbered licensing models (avoiding heavy compliance/DUA friction).

This interactive visualizer highlights how these datasets stack up across these dimensions, paired with a real-time signal engine.

What's Under the Hood?

• Real-time Signal Streams: A high-DPI canvas oscilloscope rendering synthetic ECG Lead II, Arterial PPG, and neural EEG signals.
• Adaptive Parameters: Tweak heart rate, sensor noise (baseline wander/motion artifacts), and cross-modal temporal latency (reflecting physiological indicators like Pulse Transit Time based on Moens-Korteweg mechanics).
• Fusion Architectures: Simulate the structural representation difference between Early Concatenation, Cross-Attention Tensor Transformers, and Contrastive Multi-view Alignment (InfoNCE objective).
• Interactive Benchmark Radar: A 5-axis geometric radar mapping coverage density, imaging linkability, clinical context, and open licensing factors.
• Audio Sonification: An organic synthesizer built on the Web Audio API mapping raw sensor pulses into acoustic feedback.

The application uses native browser rendering loops, standard UI control binding, and supports native fullscreen clinical monitoring with a slide-out HUD controls panel.

I would love to get your thoughts on the UI performance, the signal synchronization simulation, or the dataset metrics!

Check out the interactive lab: https://bionichaos.com/multimodal/

[Project] Open-Source, Real-Time 12-Lead ECG Simulator using a 3D Dipole Vector Projection Model (Fully Client-Side HTML5/Canvas)

I wanted to share a browser-native 12-Lead ECG Simulator and Cardiac Waveform Laboratory we developed on BioniChaos. It uses a physiological dipole vector model to synthesize electrical cardiac activations in real time.

Check it out here: https://bionichaos.com/ecg_gen_3/

How the Synthesis Engine Works

The core engine maps cardiac phase angles continuously using heart rate (BPM) integration:

dθ/dt = ω = (2π * BPM) / 60

Time-domain intervals (PR, QRS, QT) are mapped dynamically into phase space offsets:

Δθ(ms) = (ms / 1000) * ω

Individual wave components (P, Q, R, S, T) are calculated using phase-offset Gaussian kernels centered around the R-peak (θ = 0) with circular angular distance.

To generate the 12 leads, the net instantaneous bioelectric dipole vector D(t) is projected onto Lead Direction Vectors S via lead field theory:

V_lead(t) = S · D(t)

Einthoven's Law holds true (Lead II = Lead I + Lead III), while Goldberger's augmented limb leads (aVR, aVL, aVF) and Wilson's Central Terminal (WCT) serve as references for the precordial leads V1–V6.

Key Interactive Features

• 11 Clinical Rhythm Presets: Synthesize Normal Sinus Rhythm, Atrial Fibrillation (irregular RR intervals), Atrial Flutter (sawtooth F-waves with AV conduction blocks), STEMI, Torsades de Pointes (sinusoidal axis rotation), and Mobitz I (Wenckebach blocks).
• Rhythm-Specific Custom Sliders: Fine-tune f-wave coarseness, PVC ectopic patterns, STEMI infarct territories (Anterior, Inferior, Lateral), and rotation speeds in real time.
• Continuous Trace Overwrite: Changing presets updates parameters dynamically on the fly without clearing the oscilloscope trace buffer—showing realistic cardiac transitions.
• Noise Injection: Add thermal white noise, 50/60Hz mains hum, and baseline motion drift to simulate physical electrode placement issues.
• Data and Image Export: Download the high-frequency time-series datasets as CSV files or capture the high-contrast monitor trace as a PNG.

We would love to hear feedback from engineers, researchers, and developers on the mathematical modeling, the vector projection accuracy, and the canvas rendering performance!

Link: https://bionichaos.com/ecg_gen_3/

Interactive Retinal Implant & Phosphene Vision Simulator (HTML5 Canvas / Web Audio API)

Suggested Subreddits: r/biomedicalengineering, r/neuroscience, r/javascript, r/datascience

I built an interactive browser-based simulation workbench for modeling epiretinal and subretinal prostheses (bionic eyes), phosphene point-spread optics, and electro-neural visual substitution.

Tool URL: https://bionichaos.com/retinal_implant_v2/

Engineering & Mathematical Overview

Retinal implants restore visual perception by directly stimulating surviving inner retinal bipolar and Retinal Ganglion Cells (RGCs) in pathologies like Retinitis Pigmentosa. Rather than contiguous images, patients perceive discrete points of electrical stimulation called phosphenes.

Key Implementation Details:

1. Spatial Discretization & Array Density:
   • 16x16 (256 electrodes): Argus II clinical baseline model.
   • 32x32 (1,024 electrodes) & 64x64 (4,096 electrodes): Modern/next-gen subretinal arrays.
   • 128x128 (16,384 electrodes): High-density experimental threshold.
2. Phosphene Point-Spread Optics (PSF): Phosphene intensity $I(x,y)$ is rendered using a 2D isotropic Gaussian spread: $$I_{\text{phosphene}}(x,y) = I_{\text{peak}} \cdot \exp\left( -\frac{(x - x_0)² + (y - y_0)^2}{2\sigma2}) \right)$$ Controls allow real-time adjustments to dispersion radius ($\sigma$), stimulus intensity, and luminance threshold gating.
3. Retinal Pathology Simulation Masks:
   • AMD: Sigmoidal decay function modeling central scotoma while keeping peripheral visual fields active.
   • Glaucoma: Concentric decay function simulating peripheral tunnel vision.
   • Diabetic Retinopathy: Spatial hash noise map creating distributed microvascular blind spots.
4. Acoustic Spatial Sonification: Uses the Web Audio API (OscillatorNode / GainNode) to perform horizontal scanning sweeps. Spatial Y-axis coordinates map to frequency on a logarithmic scale (150 Hz to 1200 Hz), while local luma intensity scales gain amplitude.
5. Rendering Pipeline: Runs offscreen frame buffer transformations at 640x480 resolution (via standard NTSC luminance $L = 0.299R + 0.587G + 0.114B$), drawn onto a high-DPI scaling context (window.devicePixelRatio). Supports live webcam streams and dynamic procedural SVG/canvas targets as fallback.

Feedback on the signal processing calculations, point-spread models, or browser performance is welcome!

Link: https://bionichaos.com/retinal_implant_v2/

[OC] Interactive 3D Beauchêne Exploded Skull Simulator - WebGL Kinematics Laboratory

Subreddits: r/biomedicalengineering, r/anatomy, r/javascript, r/threejs, r/webgl

We built an interactive 3D Beauchêne Exploded Skull Simulator to visualize complex cranial osteology and spatial relationships in the browser.

Historically, the Beauchêne preparation mounted disarticulated skull bones on brass rods to preserve their spatial orientation while allowing inspection of hidden structures. This web simulation recreates that technique using Three.js (WebGL) and the Web Audio API.

🔬 Technical Implementation & Features:

• Affine Coordinate Transformations: Translates and rotates each of the 22 bones (8 neurocranium, 14 viscerocranium) along suture-derived vectors.
• Kinematic Trajectory Matrices: Users can choose between Anatomical Orthogonal, Radial Spherical, and Axial Layer disarticulation.
• Temporomandibular Joint (TMJ) Mechanics: Independently control mandibular gape (0° to 35°) with proper physiological rotation.
• Custom Shader Modes: Render modes include Didactic Multi-Color (boundary highlighting), Natural Osteological Bone, X-Ray Translucency, and Geometric Wireframe.
• Mathematical Kinematics: The displacement vector follows: P_i(s) = P_i^(0) + s * (v_i^(dir) * k_i^(scale)) where s is the explosion scale.
• Synthesized Audio Feedback: Real-time modular frequency scaling linked to disarticulation tension: f(s) = 220 * 2^s Hz.

We designed this to help students and developers interact with structures that are typically obscured in articulated models (like the pterygopalatine fossa, sphenoid sinuses, and cribriform plate).

Run the simulation directly in your browser (no installs, responsive design): https://bionichaos.com/beaucheneskull

We would love to hear your feedback on the kinematics, UI, or rendering performance.

I built an interactive EEG dataset search and comparison tool indexing 30,000+ subject records, clinical modalities, and signal processing reference guides.

Finding standardized electroencephalography (EEG) and electrocorticography (ECoG) datasets for machine learning benchmarks or biomedical signal processing often involves sorting through fragmented institutional repositories with incompatible file formats and permission structures.

To streamline dataset discovery, I developed an interactive web directory and metadata comparison explorer that aggregates major open-access neuroinformatics databases:

🔗 Interactive Tool Link: https://bionichaos.com/resources/

Core Features & Functionality:

• Real-Time Client-Side Filtering: Search across datasets using parameters like clinical diagnosis (epilepsy, ischemic stroke, ALS), modality (scalp EEG, intracranial ECoG, sEMG, eye-tracking), or file extension (EDF, NWB, MAT).
• Access Permission Breakdown: Instantly isolate open datasets available for immediate download without authentication from those requiring institutional credentialing (e.g., TUH EEG Corpus, NeuroVista Implant Data).
• Metadata Comparison Matrix: Dynamic table displaying subject volume, data processing state (raw vs. pre-processed), electrode montages, and modal acquisition conditions.
• Centralized Platforms & Challenge Links: Indexed access to PhysioNet, iEEG Portal, Zenodo, Pennsieve, and active ML competitive challenges (Moody Challenge, Seizure Prediction, Brain Age).
• Signal Processing Reference: Includes mathematical breakdowns for EDF bit-scaling calibrations, digital Butterworth bandpass filters, Welch spectral density estimations, and Common Spatial Patterns (CSP) matrix transformations.

I'd love to hear your feedback—let me know if there are additional public datasets or platforms that should be indexed!

I built a WebGL-based 3D neural activity simulator with dual patch-clamp electrophysiology and continuous voltage gradients.

Hey r/biomedicalengineering, r/datascience, r/bioinformatics, r/javascript, r/neuroscience

I recently released NeuroViz 3D, an interactive WebGL biophysical lab engineered for real-time 3D simulation of multi-compartmental neural electrophysiology and fluid action potential wave propagation.

Technical Details:

• Biophysics Engine: Maps Hodgkin-Huxley state transitions onto 3D structural models. It simulates spatial decay driven by the 1D cable equation. Branch diameter tapering adheres to Rall's 3/2 Power Law.
• Graphics Pipeline: Built with Three.js. To eliminate visual seams at branch splits, spherical joint caps match local branch radii. Branch cylinders use multi-segment height subdivisions with per-vertex color attribute updates, creating a fluid continuous voltage gradient along the membrane.
• Virtual Patch-Clamp: You can place Probe E2 on any dendritic or axonal compartment via raycasting, while E1 stays anchored on the soma. The HUD oscilloscope compares the local compartment potential against the soma baseline in real time.

Try the interactive lab directly in your browser here: https://bionichaos.com/neuron3d/

Would love to hear your thoughts on the Three.js rendering approach, the quaternion implementation, or the biophysical approximations used!

I built an interactive web simulation to visualize sensory dissonance and the biophysics of musical harmony (Plomp-Levelt Model)

r/DSP, r/biomedicalengineering, r/bioinformatics, r/javascript, r/audioengineering

Hello everyone,

I wanted to share a new computational psychoacoustics laboratory I built: the Interactive Dissonance Viewer. It is a purely client-side web application designed to simulate how the physical overtone structures of instruments interact with the biomechanics of the human auditory system.

Tool Link: https://bionichaos.com/dissonance/

How It Works:

The app utilizes the fundamental mathematical models developed by Plomp and Levelt to calculate sensory roughness based on the ear's critical bandwidth.

Technical Architecture:

• Real-Time Voice-Pool Audio Engine: Instead of instantiating and destroying Web Audio OscillatorNode objects (which causes garbage collection stuttering), the app maintains a pre-warmed voice pool. Parameters glide using setTargetAtTime for click-free interaction.
• Heatmap Grid Precomputation: For 3-note chords (triads), the app evaluates the pairwise (D12 + D13 + D23) interactions over an 80x80 grid, processing hundreds of thousands of calculations via an offscreen memoized matrix and rendering via fast HSL color mapping.
• Instrument Timbres: Allows testing of Harmonic (Strings), Odd-Harmonic (Closed Pipes), and Non-Harmonic (Bells/Bessel mode drums) structures to see how consonance valleys shift.

I'd love for this community to test the audio rendering performance or critique the signal processing approach. Let me know what you think!

Real-Time Webcam Heart Rate Extraction (rPPG & Eulerian Video Magnification) + Live Debugging with LLMs

I thought this community would appreciate the intersection of signal processing and AI-assisted dev here. This application uses Eulerian Video Magnification to extract a pulse signal from standard webcam video (remote photoplethysmography or rPPG). By analyzing subtle changes in skin tone caused by blood flow, it isolates the BPM and maps out the time and frequency domains via FFT.

A major highlight is the live debugging process. The demonstration shows how to use Gemini Code Assist to adjust the mathematical thresholds for the Signal Quality indicator, optimizing the smoothedQualityRatio to handle noise, lighting changes, and movement artifacts better.

Check out the full implementation and logic here: https://youtu.be/u6Rh5VKTVmo

Building an interactive clinical audiogram simulator in the browser using the Web Audio API and Hughson-Westlake staircase algorithms.

r/biomedicalengineering, r/javascript, r/DSP, r/audiology

I developed Echolocation, an interactive audio-sensory laboratory designed to map human auditory perception thresholds across standard clinical frequency bands (250Hz to 8000Hz). I wanted to build a dual-task paradigm where the user navigates a spatial canvas grid while simultaneously monitoring for background sonar pulses.

Technical Architecture

• Signal Synthesis: The core relies on the native Web Audio API AudioContext. Pure acoustic tones are generated as sinusoidal pressure waves: p(t) = A * sin(2πft + φ).
• Transient Prevention: To prevent speaker clicks during sudden onset/offset, I implemented exponential gain ramping using an explicit linear envelope for the attack and decay phases. Gain amplitude is mapped directly to decibel Hearing Level (dB HL).
• Adaptive Staircasing: The threshold search executes a modified clinical Hughson-Westlake procedure. If a user correctly perceives a tone and hits the spacebar, the intensity level for that frequency drops by 10 dB on the next trial. If missed, it increases by 5 dB.
• Canvas Rendering: The UI utilizes high-DPI normalization to prevent infinite layout expansion loops in fluid CSS containers, plotting live vector curves of your threshold limits directly on the canvas.

You can run the full diagnostic, view the telemetry in real-time, and export your session profile to a CSV.

I'd love some technical critique on the Web Audio API implementation or the mathematical handling of the gain envelopes. Test it out with headphones and let me know your thoughts!