Neurotechnology: Methods, Uses and System Types

Neurotechnology includes several different families of tools. Recording technologies include electroencephalography (EEG), which measures electrical activity from the scalp, and functional near-infrared spectroscopy (fNIRS), which estimates cortical changes in blood oxygenation and blood flow. Brain–computer interfaces (BCIs) translate measured nervous-system activity into commands, communication or feedback for an external system. Stimulation technologies include transcranial magnetic stimulation (TMS), which uses brief magnetic fields to induce electrical currents in cortical tissue, and transcranial electrical stimulation (tES), which applies low-intensity current through scalp electrodes. Biofeedback and neurofeedback return measured signals to the user in real time.

These families often overlap. A modern experiment may combine EEG, stimulation, behavioural responses and autonomic physiology in one synchronized system. Selection is therefore best guided by the scientific or clinical question, the signal of interest and the intended intervention.

A technology can be scientifically useful without being an established clinical treatment. EEG, for example, has long-standing clinical roles in epilepsy and neurophysiology, while many EEG-derived cognitive biomarkers remain research tools. TMS has evidence-based therapeutic indications, but the evidence depends strongly on disorder, target and protocol. BCI rehabilitation is promising and increasingly supported by trials, yet it remains an adjunctive and developing clinical area rather than a universal standard of care. [1–5]

A scientifically responsible description should therefore distinguish technical capability from evidence supporting diagnostic or therapeutic use.

Neurotechnology is used across clinical neurophysiology, cognitive and social neuroscience, neurorehabilitation, assistive technology, neuromodulation—methods designed to alter nervous-system activity—, human-performance research and multimodal experimental platforms.

Selection depends on the biological signal, temporal and spatial resolution, mobility, participant population, experimental environment, synchronization needs, analysis pipeline, regulatory context and available expertise. A mobile cognition study may favor EEG or fNIRS; a cortical perturbation study may require TMS; a motor-rehabilitation project may combine BCI with robotics or functional electrical stimulation.

The useful starting point is the project: population, task, outcome, budget, environment and required level of evidence. The hardware follows from that design.

Modern neurotechnology increasingly combines complementary signals. EEG can capture millisecond-scale electrical dynamics while fNIRS tracks slower haemodynamic changes. ECG, EDA, respiration and EMG can show how autonomic and muscular responses change alongside brain activity. Synchronization layers then align these streams with stimuli and behavioural responses.

This multimodal approach can answer richer questions, but it also raises the burden of timing accuracy, artefact control, data interpretation and reproducibility.

Neurotechnology projects are commonly built as single-modality, multimodal, mobile/wearable or closed-loop systems. Stimulation projects may also combine a stimulator with recording and neuronavigation.