Information Sheet: Neurotechnology and Brain-Computer Interfaces (BCIs)
Introduction
Neurotechnology encompasses a broad range of tools and methods designed to interface
with the nervous system to monitor, modulate, or enhance neural function. Among the
most transformative advances in this field are Brain-Computer Interfaces (BCIs), which
establish direct communication pathways between the brain and external devices. BCIs
have enormous potential to revolutionize medical treatment, rehabilitation, communication,
and human augmentation by bypassing damaged neural pathways and enabling direct
control of computers, prosthetics, and other systems via neural signals.
Historical Background
The concept of interfacing with the brain dates back to the 1960s when basic neural
recording techniques were developed. Early BCIs emerged in the 1970s and 1980s with
rudimentary devices enabling limited communication and control in animal models. Rapid
progress in neurophysiology, microelectronics, and computational neuroscience in the
21st century has led to sophisticated BCIs capable of decoding complex neural activity in
real time. Milestones include the first human trials demonstrating neural control of robotic
limbs and communication devices.
Principles of Neurotechnology and BCIs
1. Neural Signal Acquisition
BCIs rely on capturing neural activity through various modalities:
Invasive methods: Implanted microelectrode arrays directly record electrical activity from
neurons in the cortex, offering high resolution and specificity but with surgical risks.
Partially invasive methods: Electrocorticography (ECoG) electrodes placed on the brain
surface balance invasiveness and signal quality.
Non-invasive methods: Electroencephalography (EEG), functional near-infrared
spectroscopy (fNIRS), and magnetoencephalography (MEG) detect brain signals externally
but with lower spatial and temporal resolution.
2. Signal Processing and Decoding
Raw neural data undergo filtering, amplification, and feature extraction to identify patterns
associated with intended movements, thoughts, or commands. Machine learning
algorithms and neural networks decode these signals to translate brain activity into
actionable outputs.
3. Output and Feedback
Decoded signals control external devices such as cursors, robotic arms, wheelchairs, or
computer interfaces. Closed-loop BCIs provide real-time sensory feedback, enhancing
precision and user adaptation.
Applications of Neurotechnology and BCIs
A. Medical and Rehabilitation
Restoring Motor Function: BCIs enable paralyzed patients to control prosthetic limbs,
exoskeletons, or computers through thought alone.
