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Neurotechnologia

EEG

1929ActivePublished: 27 July 2026Updated: 27 July 2026Published
Key innovation
A non-invasive, direct measurement of the brain's electrical activity with high temporal resolution (milliseconds), providing the core signal for brain-computer interfaces.
Category
Neurotechnologia
Abstraction level
Building block
Operation level
DataApplicationSystem
Use cases
Brain-computer interfaces (BCI), including passive BCINeuroadaptive technologyNeurological diagnostics (e.g. epilepsy, sleep)Monitoring cognitive load, attention and fatigueNeurofeedbackBrain research (neuroscience)

How it works

1) Electrodes placed on the scalp (e.g. per the 10-20 system) record potential differences produced by neuronal activity. 2) The weak signals (microvolts) are amplified and sampled (analog-to-digital conversion). 3) The signal is filtered and cleaned of artifacts (motion, blinks, EMG, 50/60 Hz mains). 4) Features are extracted (band power, ERP, connectivity) and then interpreted or classified (e.g. by a machine-learning model) for BCI or mental-state analysis.

Problem solved

Enables direct, non-invasive real-time readout of brain activity, providing an objective signal about the user's mental states and intentions that cannot be obtained from conventional input devices.

Components

ElectrodesSignal acquisition

Scalp sensors (wet or dry) recording the brain's electrical potentials.

Wet (gel) electrodesHigher signal quality, require preparation.
Dry electrodesComfortable, for everyday use (e.g. wearables).

Official

Amplifier and ADCAmplification and digitization

Amplifies the weak (microvolt) signals and samples them into digital form.

Official

Signal processingSignal analysis

Filtering, artifact removal and feature extraction (bands, ERP) from the EEG signal.

Official

Implementation

Reference implementations
Zander Labs — Zypher EEG suite
Zander Labs
Implementation pitfalls
Artifacts and low SNRHigh

The EEG signal is weak (microvolts) and prone to motion, muscle and mains artifacts.

Fix:Filtering, artifact removal (ICA), shielding, good electrode contact.
Limited spatial resolutionMedium

EEG localizes brain sources poorly (smearing through the skull).

Fix:More channels, source-reconstruction methods, fusion with other modalities.

Evolution

Original paper · 1929 · Archiv für Psychiatrie und Nervenkrankheiten, 1929 · Hans Berger
On the Electroencephalogram of Man
Hans Berger
1924
First human EEG recording (Hans Berger)
Inflection point

Hans Berger records the first human electroencephalogram.

1929
Publication on human EEG

Berger publishes "Über das Elektroenkephalogramm des Menschen".

2011
EEG as the passive-BCI signal

EEG becomes the core signal for passive BCIs and neuroadaptive technology (Zander & Kothe).

Hyperparameters (configurable axes)

Number of channelsHigh

Number of electrodes (e.g. 1-8 for wearables, 32-256 for research).

8Mobile/wearable.
64Research.
Electrode typeHigh

Wet (gel) vs dry.

dryEveryday use.
Sampling rateMedium

E.g. 250-1000 Hz.

256 HzTypical.

Computational complexity

Time complexity: O(kanaly × probki) na okno. Space complexity: O(kanaly × bufor).

Compute bottleneck

Signal quality and artifacts

Low signal-to-noise ratio (microvolts), artifacts (motion, blinks, EMG, mains) and limited spatial resolution are the main limitations, not compute.

Depends on
Kontakt/impedancja elektrodArtefakty ruchowe/miesniowe

Execution paradigm

Primary mode
Dense

Continuous, parallel sampling of all channels.

Activation pattern
All paths active
Routing mechanism

Continuous signal acquisition, no routing.

Parallelism

Parallelism level
Fully parallel

Channels and features can be processed independently/in parallel.

Scope
Inference
Constraints
!Acquisition is continuous and must keep up with the signal in real time.

Hardware requirements

Primary

EEG acquisition and processing are lightweight; they run on CPU/embedded devices. The key is dedicated analog hardware (electrodes + amplifier).