How Are Biosignals Measured? Inside the Sensors That Read Your Mind and Body

Every thought, heartbeat, and muscle twitch leaves behind a signal, but how do we actually capture them? In this blog post, we explore the sensors that make biosignal measurement possible, from EEG and ECG electrodes to optical and biochemical interfaces, and what it takes to turn those signals into meaningful data.

When we think of sensors, we often imagine cameras, microphones, or temperature gauges. But some of the most fascinating sensors aren’t designed to measure the world, they’re designed to measure you.

These are biosignal sensors: tiny, precise, and increasingly powerful tools that decode the electrical whispers of your brain, heart, and muscles. They're the hidden layer enabling brain-computer interfaces, wearables, neurofeedback systems, and next-gen health diagnostics.

But how do they actually work? And what makes one sensor better than another?

Let’s break it down, from scalp to circuit board.

First, a Quick Recap: What Are Biosignals?

Biosignals are the body’s internal signals, electrical, optical, or chemical , that reflect brain activity, heart function, muscle movement, and more. If you’ve read our earlier post on biosignal types, you’ll know they’re the raw material for everything from brain-computer interfaces to biometric wearables.

In this blog, we shift focus to the devices and sensors that make it possible to detect these signals in the real world, and what it takes to do it well.

The Devices That Listen In: Biosignal Sensor Types

A Closer Look: How These Sensors Work

1. EEG / ECG / EMG – Electrical Sensors

These measure voltage fluctuations at the skin surface, caused by underlying bioelectric activity.

It’s like trying to hear a whisper in a thunderstorm; brain and muscle signals are tiny, and will get buried under noise unless the electrodes make solid contact and the amplifier filters aggressively.

There are two key electrode types:

Wet electrodes: Use conductive gel or Saline for better signal quality. Still the gold standard in labs.
Dry electrodes: More practical for wearables but prone to motion artifacts and noise (due to higher electrode resistance).

Signal acquisition often involves differential recording and requires high common-mode rejection ratios (CMRR) to suppress environmental noise.

Fun Fact: Even blinking your eyes generates an EMG signal that can overwhelm EEG data. That’s why artifact rejection algorithms are critical in EEG-based systems.

2. Optical Sensors (PPG, fNIRS)

These use light to infer blood flow or oxygenation levels:

PPG: Emits light into the skin and measures reflection, pulsatile blood flow alters absorption.
fNIRS: Uses near-infrared light to differentiate oxygenated vs. deoxygenated hemoglobin in the cortex.

Example: Emerging wearable fNIRS systems like Kernel Flow and OpenBCI Galea are making brain oxygenation measurement accessible outside labs.

3. Galvanic Skin Response / EDA – Emotion’s Electrical Signature

GSR (also called electrodermal activity) sensors detect subtle changes in skin conductance caused by sweat gland activity, a direct output of sympathetic nervous system arousal. When you're stressed or emotionally engaged, your skin becomes more conductive, and GSR sensors pick that up.

These sensors apply a small voltage across two points on the skin and track resistance over time. They're widely used in emotion tracking, stress monitoring, and psychological research due to their simplicity and responsiveness.

Together, these sensors form the foundation of modern biosignal acquisition — but capturing clean signals isn’t just about what you use, it’s about how you use it.

How Signal Quality Is Preserved

Measurement is just step one; capturing clean, interpretable signals involves:

Analog Front End (AFE): Amplifies low signals while rejecting noise.
ADC: Converts continuous analog signals into digital data.
Signal Conditioning: Filters out drift, DC offset, 50/60Hz noise.
Artifact Removal: Eye blinks, jaw clenches, muscle twitches.

Hardware platforms like TI’s ADS1299 and Analog Devices’ MAX30003 are commonly used in EEG and ECG acquisition systems.

New Frontiers in Biosignal Measurement

Textile Sensors: Smart clothing with embedded electrodes for long-term monitoring.
Biochemical Sensors: Detect metabolites like lactate, glucose, or cortisol in sweat or saliva.
Multimodal Systems: Combining EEG + EMG + IMU + PPG in unified setups to boost accuracy.

A recent study involving transradial amputees demonstrated that combining EEG and EMG signals via a transfer learning model increased classification accuracy by 2.5–4.3% compared to EEG-only models.

Other multimodal fusion approaches, such as combining EMG and force myography (FMG), have shown classification improvements of over 10% compared to EMG alone.

Why Should You Care?

Because how we measure determines what we understand, and what we can build.

Whether it's a mental wellness wearable, a prosthetic limb that responds to thought, or a personalized neurofeedback app, it all begins with signal integrity. Bad data means bad decisions. Good signals? They unlock new frontiers.