HIGGSION
PRODUCT ARCHITECTURE//HG-BRAINWAVE-SPEC

HiggSion Brainwave:
Decoding Imagined Speech into Text

A non-invasive brain-computer interface designed to translate intentional inner speech into readable text in real time — with no vocalization, no lip movement, and no character-grid selection.

CURRENT STATUS: Early research stage — working toward Stage 1 vocabulary validation
01.0//CORE CONCEPT

Direct Inner-Speech Translation

Translating silent thought dynamics into computational text stream.

When a person silently formulates words in their mind without speaking aloud, synchronized neural assemblies in the cortex produce distinct spatio-temporal electrical patterns. Non-invasive EEG captures these microvolt fluctuations across the scalp.

HiggSion Brainwave is built to decode those intentional inner-speech signals into text. It does not require eye-gaze tracking, screen-based character keyboards, or facial muscle activation. You think the word; the interface produces the text.

Non-invasive imagined-speech decoding is an open, complex research problem. Cortical signals are heavily attenuated by the skull and corrupted by physiological noise. Our engineering addresses this bottleneck through personalized neural modeling and dedicated edge compute.

Personalized Architecture

Not a Universal Mind-Reader

Every human brain possesses unique cortical folding, skull geometry, and cognitive activation dynamics. A single universal model cannot reliably decode imagined words across different people.

1. Foundation Base ModelTrained on multi-user EEG corpora to learn general neural representations.
2. Personal Neural AdapterCalibrated per user to map individual cortical signatures accurately.
02.0//SYSTEM PIPELINE

End-to-End Processing Flow

From scalp bio-potential acquisition to local on-device text output.

PIPELINE // HIGGSION BRAINWAVE END-TO-END ARCHITECTURE
01 // ACQ

Raw EEG Acquisition

Low-noise multichannel surface potential capture.

  • Electrode skin interface
  • 24-bit ADC sampling
  • Active shielding
↓INFERENCE FLOW
02 // PRE

Artifact Filtering

Suppressing biological & electrical contamination.

  • 50/60Hz notch rejection
  • 0.5–50Hz bandpass
  • Ocular/EMG filtering
↓INFERENCE FLOW
03 // BASE

Foundation Decoder

General neural representation extraction.

  • Cross-subject base model
  • Spatial covariance
  • Candidate generation
↓INFERENCE FLOW
04 // ADAPT

Personal Adapter

User-specific neural calibration weights.

  • Per-user fine-tuning
  • Session drift tracking
  • On-device calibration
↓INFERENCE FLOW
05 // OUT

Context & Text Output

Real-time ambiguity resolution into text.

  • Local LM refinement
  • <100ms edge latency
  • Decoded text stream
* Real-time inference executed on-device (phone/PC) with sub-100ms latency.SYS_ARCH: BASE + ADAPTER // LOCAL EDGE
03.0//SOFTWARE ARCHITECTURE

Two-Layer Intelligence & Edge Privacy

Decoupling neural decoding from contextual language refinement while keeping raw data strictly local.

DUAL-LAYER DECODING

Neural Decoder + Contextual Language Layer

Brainwave separates intent extraction into two specialized computational layers:

Layer 1: Neural DecoderProcesses raw EEG features and outputs candidate speech units with confidence distributions.
Layer 2: Contextual Language ModelUses conversational context to resolve ambiguity among candidate words. The language model never reads the brain directly — it only refines the neural decoder's output.
EDGE PROCESSING & PRIVACY

Local Inference on Phone / PC

Real-time inference runs entirely on the user's local host device (phone or PC). This architecture was chosen for two critical reasons:

Sub-100ms LatencyEliminating cloud roundtrips enables instant feedback, allowing natural human-algorithm co-adaptation.
Strict Data PrivacyRaw EEG signals never leave the local device during active use. Central cloud compute is used only for pretraining the base model on aggregated research datasets.
04.0//SOFTWARE ROADMAP

Staged Vocabulary Expansion

A phased development path from closed-set vocabulary to continuous inner speech.

STAGE 1 // ACTIVE

Fixed Core Vocabulary

Discrete classification of an initial 8-word functional vocabulary:

YES • NO • HELP • STOP • WATER • HOME • GO • CALL
CURRENT FOCUS
STAGE 2 // PLANNED

Vocabulary Expansion

Scaling discrete word classes from 8 to 20, 50, and 100+ high-frequency command terms.

DISCRETE EXPANSION
STAGE 3 // RESEARCH

Phoneme Encoding

Moving from whole-word classes to subword and phoneme representations for scalable open-vocabulary coverage.

SUBWORD REPRESENTATIONS
STAGE 4 // LONG-TERM

Continuous Speech

Decoding full, continuous inner-speech sentences in real time. This is our ultimate destination, not current state.

LONG-TERM GOAL
05.0//HARDWARE VISION

Custom Headset Engineering

Designing an ergonomic, low-noise EEG acquisition wearable from first principles.

Off-the-shelf consumer headsets are not built for silent speech research — they suffer from high contact noise, rigid electrode placement, and proprietary closed firmware.

We evaluate and benchmark against the OpenBCI Cyton open-source architecture to establish clean baseline signal metrics, derive our own acquisition requirements, and design a custom acquisition PCB tailored specifically for imagined speech montages.

The early headset handles signal acquisition and low-latency transmission only. All decoding is offloaded to the host device, keeping the wearable lightweight, energy-efficient, and thermally stable.

// Onboard Status Display

Telemetry & System Diagnostics Only

The headset features an integrated status screen dedicated exclusively to device telemetry — battery status, electrode contact impedance, wireless connection, and calibration progress. It is not an input interface for character selection.

// Hardware Roadmap Stages
H0
Cyton Evaluation:Benchmarking OpenBCI Cyton reference hardware to validate noise floor and temporal resolution.
[COMPLETED]
H1
Experimental Prototype:Building benchtop test fixtures and electrode montages on reference acquisition hardware.
[ACTIVE]
H2
Custom EEG Acquisition PCB:Designing custom analog front-end with ultra-low noise amplifiers and integrated power management.
[IN DESIGN]
H3
Custom Headset Enclosure:3D mechanical prototype with ergonomic spring-loaded dry/semi-dry electrode mounts.
[PLANNED]
H4
Integrated Engineering Prototype:Complete integration of custom PCB, battery management, wireless link, and status display.
[PLANNED]
H5
Product Prototype:Refined consumer-grade wearable headset ready for multi-subject validation trials.
[LONG-TERM]
RESEARCH TRANSPARENCY

HiggSion Brainwave is an active, early-stage research initiative. We do not claim commercial readiness or full-sentence continuous decoding today. All development proceeds systematically through verifiable empirical milestones.

Discuss Brainwave Research & Collaboration

We welcome dialogue with neuroscientists, machine learning researchers, and deep-tech partners.

CONTACT ENGINEERING