HIGGSION
R&D DIVISION//SYS_REF: HG-2026-X
HIGGSION

Engineering
the Unseen

An independent deep-tech R&D venture developing Brainwave — a non-invasive neural interface to decode imagined speech into text.

EXPLORE RESEARCHBRAINWAVE SYSTEM
Core MethodologyModel → Simulate → Validate
Operating ModeIndependent Exploration
Active FrontierImagined Speech BCI
// 01.0 DISCIPLINARY FOCUS

Investigating the outer boundaries of neural signal computation.

Higgsion is an independent deep-tech research and development venture. We are building HiggSion Brainwave — a non-invasive brain-computer interface designed to decode intentional imagined speech into readable text in real time.

Our engineering efforts concentrate across four technical pillars:

01 // NEURAL FOUNDATION

Base Representation Models

Multi-user EEG pretraining learning universal neural feature dynamics.

02 // ADAPTER ARCHITECTURE

Personalized Calibration

Lightweight per-user neural adapters tuned to individual cortical signatures.

03 // EDGE INFERENCE

Local Real-Time Execution

Sub-100ms on-device processing ensuring strict privacy with zero raw data export.

04 // HARDWARE TELEMETRY

Custom Acquisition Headset

Low-noise bio-potential acquisition benchtop hardware evolving toward custom wearable PCB.

02.0//ACTIVE FRONTIER

HiggSion Brainwave

Developing non-invasive silent speech decoding from scalp electroencephalography.

ACTIVE RESEARCH FOCUS
SILENT SPEECH DECODING

Decoding Imagined Speech into Text

HiggSion Brainwave translates intentional inner speech — silent thoughts without vocal cord activation, lip movement, or visual character selection — into structured text.

// PERSONALIZED, NOT UNIVERSALBrainwave is not a universal mind-reader. A centralized base model learns general neural representations, while individual calibration trains personal neural adapters for each specific user.
// TWO-LAYER INTELLIGENCEA neural decoder generates candidate speech units from microvolt EEG signals, while a localized language model uses context to resolve ambiguity.
EXPLORE BRAINWAVE SPECIFICATION
HARDWARE ARCHITECTURE

Custom Acquisition Headset

Rather than repackaging consumer headsets, we benchmark reference open hardware (OpenBCI Cyton) to derive custom EEG acquisition requirements for our own low-noise PCB and ergonomic wearable design.

ONBOARD TELEMETRY DISPLAYDedicated status screen displays signal impedance, battery, and calibration state — never for character-grid input.
STAGED RESEARCH ROADMAP
STAGE 1 (ACTIVE)Fixed 8-Word Core Vocabulary
STAGE 2Vocabulary Expansion (20 → 100+ Words)
STAGE 3Phoneme & Subword Encoding
STAGE 4Continuous Imagined Speech
03.0//ENGINEERING PRINCIPLE

Methodology Pipeline

Our research follows a disciplined, first-principles workflow to verify biological signal constraints before committing to production hardware.

Intellectual HonestyNon-invasive imagined-speech decoding is an open research problem. We state our current development milestones transparently.
01

Electrophysiological Modeling

Quantifying skull volume conduction, electrode skin impedance, and cortical signal attenuation.

02

Reference Benchmarking

Evaluating multi-channel acquisition performance on OpenBCI Cyton reference architecture.

03

Model Training & Personalization

Training centralized foundation representations and lightweight per-user neural adapters.

04

Custom PCB & Headset Engineering

Designing dedicated low-noise analog front-ends and wearable ergonomics.

05

Real-Time Empirical Validation

Verifying low-latency on-device decoding across Stage 1 vocabulary benchmarks.

// COLLABORATION & INQUIRIES

Interested in research or engineering collaboration?

We welcome dialogue with neuroscientists, machine learning engineers, and aligned research partners.

DIRECT INQUIRIES