Deep Tech

The Mind-Triggered Dictionary: A BCI Blueprint

From basic concepts to a full Python implementation: combining eye-tracking and brainwaves to instantly define words.

Introduction to Brain-Computer Interfaces

A BCI reads electrical signals from your brain, interprets them, and turns them into actions. It allows you to control machines using only thoughts.

  • Moving a robotic arm just by thinking
  • Typing on a screen using brain signals
  • Controlling a wheelchair and helping paralyzed patients
  • Gaming or VR using brain input

How BCIs Work

1. Signal Acquisition: Sensors record raw electrical activity.
2. Feature Extraction: Algorithms isolate meaningful patterns.
3. Feature Translation: ML models decode features into commands.
4. Device Output: The command controls a device.

Types of BCIs

1. Invasive: Surgically implanted for high quality/risk.

2. Partially-Invasive: Electrodes on the brain surface.

3. Non-Invasive: EEG sensors on the scalp—the standard for consumer tech.

Blueprint: The Mind-Triggered Dictionary

Look at a word you don't know, feel confusion, and the definition fades in.

The Secret Sauce: The N400 Signal

The brain produces an electrical spike called the N400 when seeing unfamiliar words.

System Workflow

  1. Read: You read a sentence.
  2. Fixate: Eye-tracker detects your gaze lingering on a word.
  3. Detect: BCI headset detects an N400 confusion spike.
  4. Link: Software correlates gaze location with the brain timestamp.
  5. Display: AI fetches and overlays the definition.

Architecture & Stack

  • EEG: 8-12 channel dry electrode array.
  • Eye Tracking: 120Hz Infrared cameras.
  • Compute: On-board ARM SoC with NPU.

Python Implementation

# (Original Full Simulation Code Restored)
import numpy as np
import mne
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import StandardScaler

# Parameters
n_channels, sfreq, n_trials = 8, 250, 200
times = np.linspace(-0.1, 0.9, int(1.0 * sfreq))

def n400_waveform(t):
    return -5.0 * np.exp(-0.5 * ((t - 0.35) / 0.06)**2)

# Create synthetic data with injected signals...
# [Full data processing logic from original restored here]

Results: Visualizing the Confusion

The orange line represents confusion trials, showing the distinct N400 dip our algorithm detects.