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Brain–computer interface

History and Advancements in BCI Technology: – Hans Berger discovered human brain electrical activity in 1924, leading to the development of electroencephalography (EEG). – Jacques […]

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History and Advancements in BCI Technology:

– Hans Berger discovered human brain electrical activity in 1924, leading to the development of electroencephalography (EEG).
– Jacques Vidal coined the term BCI and initiated research on direct communication between brain activity and external devices.
– BCIs have evolved from non-invasive methods like EEG to invasive approaches using microelectrode arrays.
– Research on BCIs began in the 1970s, showcasing high success in classifying mental and emotional states.
– DARPA’s funding through the BRAIN initiative since 2013 has significantly boosted BCI technology development.

Animal BCI Research and Future Prospects:

– Successful recording of signals from monkey and rat cerebral cortices for BCIs has shown the potential for movement without motor output.
– Animal BCI research contributes to understanding brain-machine communication, paving the way for advancements in human BCI technology.
– Studies suggest neural stimulation can restore functional connectivity through BCI technologies, showing promise in restoring functions and enabling new capabilities.
– Continued research and funding from universities and research centers are crucial for the development of BCI applications.

Neuroprosthetics and BCI Comparison:

– Both neuroprosthetics and BCIs aim to restore functions of impaired nervous systems.
– Cochlear implants are widely used neuroprosthetic devices, while BCIs focus on direct communication pathways between brain activity and external devices.
– Experimental methods and surgical techniques are similar in neuroprosthetics and BCIs, with a focus on replacing impaired sensory organs or functions.

Prominent BCI Research Successes and Ongoing Developments:

– Notable achievements include the development of the first intracortical brain-computer interface and decoding neuronal firings to reproduce visual images.
– Researchers have demonstrated BCIs to help patients with speech impairment using neural activity and deep learning methods.
– Ongoing research aims to improve BCI accuracy, safety, and communication capabilities through collaborations between researchers and engineers.

Challenges, Considerations, and Technical Aspects of BCIs:

– Ethical considerations, accessibility, affordability, interdisciplinary collaboration, data privacy, and security are crucial factors in BCI development.
– Technical challenges in invasive BCIs include signal stability, material science impacts, and electronic limitations requiring signal conversion.
– Partially invasive BCIs and endovascular BCIs offer alternatives with improved signal quality and reduced risks compared to fully invasive methods.
– ECoG technology provides higher signal fidelity and spatial resolution, showing promise in real-world applications.
– Recent advances in decoding with BCIs have significantly improved communication rates for paralyzed individuals, with potential for even higher information transfer rates.

Brain–computer interface (Wikipedia)

A brain–computer interface (BCI), sometimes called a brain–machine interface (BMI) or smartbrain, is a direct communication pathway between the brain's electrical activity and an external device, most commonly a computer or robotic limb. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. They are often conceptualized as a human–machine interface that skips the intermediary component of the physical movement of body parts, although they also raise the possibility of the erasure of the discreteness of brain and machine. Implementations of BCIs range from non-invasive (EEG, MEG, MRI) and partially invasive (ECoG and endovascular) to invasive (microelectrode array), based on how close electrodes get to brain tissue.

Research on BCIs began in the 1970s by Jacques Vidal at the University of California, Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA. Vidal's 1973 paper marks the first appearance of the expression brain–computer interface in scientific literature.

Due to the cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels. Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s.

Recently, studies in human-computer interaction via the application of machine learning to statistical temporal features extracted from the frontal lobe (EEG brainwave) data has had high levels of success in classifying mental states (relaxed, neutral, concentrating), mental emotional states (negative, neutral, positive), and thalamocortical dysrhythmia.

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