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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Updated: Mar 10, 2026

Assessment and Communication for People with Disorders of Consciousness

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

The Berlin Brain-Computer Interface: Progress Beyond Communication and Control.

Benjamin Blankertz¹, Laura Acqualagna², Sven Dähne³

¹Neurotechnology Group, Technische Universität BerlinBerlin, Germany; Bernstein Focus: NeurotechnologyBerlin, Germany.

Frontiers in Neuroscience

|December 6, 2016

Summary

This summary is machine-generated.

Advancements in decoding brain signals offer neurotechnological potential. While Brain-Computer Interface (BCI) applications require further development for real-world complexity, BCIs are proving valuable as research tools.

Keywords:

Brain-Computer Interfacing (BCI)cognitive neuroscience covert user states electroencephalography (EEG)implicit information machine learning mental workload video quality

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Area of Science:

Neuroscience
Cognitive Science
Biomedical Engineering

Background:

Neurocognitive processes and brain signal decoding are advancing.
This progress opens avenues for neurotechnological applications.

Approach:

Review of developments in neurotechnology and Brain-Computer Interface (BCI).
Assessment of the maturity and applicability of various BCI examples.

Key Points:

BCI technology is not yet ready for general computer interaction or safety-critical workplaces.
BCIs have demonstrated utility as research tools in specific domains.
Significant effort is needed to adapt neurotechnologies for real-world complexity.

Conclusions:

Neurotechnology, particularly BCI, holds significant potential but faces challenges in practical implementation.
Further research and development are crucial to overcome the gap between current capabilities and real-world demands.