Brain computer interface

Interface between the brain and external devices

Brain–computer interface

Diagram of a brain–computer interface

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Inception
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A brain–computer interface (BCI), also known as a brain–machine interface (BMI), is a direct communication pathway between an enhanced or wired brain and an external device. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions.

History

The concept of BCIs emerged in the 1970s, with the first successful experiments conducted on animals. The field gained momentum in the 1990s with advances in neuroscience and computer science. Early research focused on electroencephalography (EEG) to record brain activity and translate it into commands for computers or prosthetic devices.

Types of BCIs

BCIs can be classified into two main types based on the method of signal acquisition:

Invasive BCIs

Invasive BCIs involve implanting electrodes directly into the brain tissue. These devices provide high-resolution signals and are often used in clinical settings for patients with severe disabilities. Examples include the Utah array and ECoG (electrocorticography) grids.

Non-invasive BCIs

Non-invasive BCIs use external sensors to detect brain activity. These are less risky and more commonly used in research and consumer applications. Techniques include EEG, functional near-infrared spectroscopy (fNIRS), and magnetoencephalography (MEG).

Applications

BCIs have a wide range of applications, including:

Medical

• Neuroprosthetics: BCIs can control prosthetic limbs, allowing amputees to regain mobility.
• Communication: BCIs enable communication for individuals with locked-in syndrome or severe motor disabilities.
• Neurorehabilitation: BCIs assist in the rehabilitation of stroke patients by promoting neural plasticity.

Non-medical

• Gaming: BCIs are used in video games to create immersive experiences.
• Smart environments: BCIs can control smart home devices, enhancing accessibility for individuals with disabilities.

Challenges

Despite significant advancements, BCIs face several challenges:

• Signal noise: Non-invasive methods often suffer from low signal-to-noise ratios.
• Ethical concerns: Issues related to privacy, consent, and the potential for misuse of technology.
• Scalability: Developing BCIs that are affordable and accessible to a wider population.

Future Directions

Research is ongoing to improve the accuracy, reliability, and usability of BCIs. Emerging technologies such as machine learning and artificial intelligence are being integrated to enhance signal processing and interpretation.

Also see

• Neurotechnology
• Neuroprosthetics
• Electroencephalography
• Neurorehabilitation
• Brain mapping