Brain Fog Insight Companion

A dual-end system (desktop host + wearable band) that detects brain-fog-related states, suggests actionable interventions, and verifies whether the intervention helps.

Note: this project is an assistive wellness tool for status estimation and behavior guidance. It is not a medical diagnostic device.

Project Overview

Brain fog is a non-diagnostic but widely used term for a cluster of cognitive symptoms: unstable attention, slower thinking speed, short-term memory decline, emotional irritability, and persistent mental fatigue. Most people can feel it immediately, but cannot explain why it happens, what type it is, or which intervention truly works for them.

I want this project to support both immediate relief and long-term lifestyle change. The immediate goal is to detect a brain-fog episode early and guide users toward concrete actions in minutes. The long-term goal is to build healthier rhythms over weeks and months: better sleep consistency, better stress recovery, and better focus hygiene in highly digital, high-pressure daily environments.

The social motivation is practical: cognitive fatigue and brain-fog-like states reduce productivity at scale and also reduce quality of life at the individual level. Similar to fatigue-related safety risks, this is not only a personal discomfort issue but also an economic and public-health challenge. Many people know the feeling of "working while mentally blurred" for long periods, yet available tools are often fragmented or reactive.

The project logic follows a core theory: brain fog can be interpreted as functional overload or dysregulation in one or more working brain networks under continuous cognitive, emotional, and environmental stress. Based on this view, the system combines multimodal sensing and graded interventions to reduce overload and restore regulation capacity.

Existing intervention and biofeedback research suggests that structured breathing, guided micro-movement, stimulus-based attention reset, and personalized behavioral feedback can improve symptoms often reported as brain fog, including mood instability and memory complaints. This project applies that evidence in an engineering format that users can operate repeatedly in real life.

Therefore, this project turns subjective feeling into a measurable and testable loop: sense -> estimate -> intervene -> re-test -> personalize. The system computes a Brain Fog Index (BFI, 0-100), identifies likely trigger layers, then provides adaptive interventions and tracks effectiveness over time, so users can gradually move from passive coping to active self-regulation.

Design sketch for the brain fog detection and intervention device

Functional Architecture

1) Multi-modal Monitoring (Parallel)

Desktop side: blink frequency, gaze duration, posture, and interaction-task response.
Wearable side: HRV, heart-rate trend, activity level, and sleep-related signals.
Context channels: optional light/noise/environmental disturbance tags.

2) AI Fusion and Cause Layering

Data fusion engine combines physiological + behavioral + contextual features.
Brain Fog Index (BFI) outputs severity levels (mild / moderate / severe).
Likely trigger categories: sleep deficit, chronic stress load, hormonal rhythm shift, and environment interference.

3) Tiered Intervention Engine

Level 1: attention reset, paced breathing (4-7-8), short micro-movement prompts.
Level 2: synchronized 40Hz audio-visual stimulation on desktop (with intensity/time safety limits).
Level 3: wearable-guided execution with vibration, voice/animation cues, and IMU-based completion check.

4) Closed-loop Evaluation

Automatic before/after comparison: BFI delta, HRV recovery, blink normalization, and compliance score.
Intervention strategy is updated using session history to improve personalization.
Long-term record shows high-risk periods, trigger patterns, and best-performing interventions.

Hardware and Build Plan

Host board: ESP32-S3 custom control board with display, camera, audio, and stimulation interfaces.
Wearable board: low-power MCU board with PPG, IMU, vibration motor, battery, and charging management.
Communication: BLE between host and wearable, with local logging for offline robustness.
Structure: desk-side enclosure + 3D printed wearable shell and dock.

Milestones

M1: stable sensing pipeline and synchronized host-band communication.
M2: BFI V1 model (rule-based fusion) and intervention scheduler.
M3: integrated closed-loop demo with measurable pre/post improvement report.

Expected Impact

The goal is to provide a practical "mental clarity companion" that helps users notice early cognitive fatigue, take targeted actions, and build healthier work-rest rhythms. Instead of one-time advice, the project focuses on repeatable sensing and evidence-based adjustment.