Short Communication - (2023) Volume 5, Issue 1
Received: 31-Jan-2023, Manuscript No. jbtc-23-16029; Editor assigned: 02-Feb-2023, Pre QC No. jbtc-23-16029 (PQ); Reviewed: 16-Feb-2023, QC No. jbtc-23-16029; Revised: 21-Feb-2023, Manuscript No. jbtc-23-16029 (R); Published: 28-Feb-2023, DOI: 10.35841/jbtc.23.5.04
As real-time bidirectional links between living brains and actuators, brain-computer interfaces have demonstrated promising potential. The field of brain-computer interfaces s has been accelerated by Artificial Intelligence (AI), which can improve the analysis and decoding of neural activity. A wide range of brain-computer interfaces applications with AI support has emerged over the past ten years. These “smart” brain-computer interfaces, which include motor and sensory brain-computer interfaces, have demonstrated significant clinical success, enhanced the quality of life of paralyzed patients, increased common people’s athletic abilities, and sped up the development of robots and discoveries in neurophysiology. In any case, despite mechanical upgrades, challenges stay as to the long preparation time frames, constant criticism, and checking of brain-computer interfaces. The authors of this article discuss advancements in brain-computer interfaces applications, their challenges, and potential future directions, as well as a review of the current state of AI as applied to brain-computer interfaces. With the huge blast in innovation, the line between people and machines has started to limit. With the assistance of machines, our fantastic science fiction about “mind control” has gradually come to pass.
Artificial Intelligence (AI) and brain-computer interfaces are the new techniques’ frontiers. AI and brain-computer interfaces experimental paradigms were typically developed and implemented separately. However, scientists now prefer to combine brain-computer interfaces and AI, which enables efficient use of the electric signals generated by the brain to control external devices [1]. The development of brain-computer interfaces may be the most significant technological advancement in decades for people with severe disabilities. People with neurodegenerative diseases like amyotrophic lateral sclerosis or acquired brain injuries may benefit from a muscle-independent communication channel provided by brain-computer interfaces, which are technologies designed to communicate with the central nervous system, as well as neural sensory organs [2]. The development of new electrophysiological methods for recording extracellular electrical activity, which is caused by variations in the electric potential carried by ions across the membranes of each neuron, is directly related to the history of brain-computer interfaces. There are both invasive and non-invasive methods for identifying various brain signals. Microelectrode arrays (MEAs), electrocorticography (ECoG), and other invasive recording techniques are examples. Electroencephalography (EEG), magnetoencephalography, functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy are examples of non-invasive brain-computer interfaces that do not pose a threat to tissue damage and are simple to use [3]. Brain-computer interfaces can be quickly used to “read” the brain to record its activity and decipher its meaning and to “write” to the brain to manipulate activity in specific regions and affect their function with the assistance of these electrophysiological techniques [4]. However, there are limitations to the development of brain-computer interfaces. We have gathered a lot of information from multiple extracellular electrodes, but it is not possible to transfer this much information effectively. From the background electrical activity that is recorded in the brain, neuroscientists are unable to precisely match a person’s intentions with the actions of a robotic arm. The justification behind this impediment is that the brain associates of mental peculiarities are estimated and inadequately comprehended [5].
AI is a collection of general methods that eventually matches or even exceeds human performance in task-specific applications by using a computer to model intelligent behavior with minimal human intervention. Internal parameters like pulse durations and amplitudes, stimulation frequencies, energy consumption by the device, stimulation or recording densities, and electrical properties of the neural tissues are constantly provided to the algorithms when AI works within brain-computer interfaces. AI algorithms can simultaneously produce the desired functional outcomes and identify useful parts and logic in the data after receiving the information.
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The author has declared no conflict of interest.
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Citation: Mathew J (2023) Applications and Challenges within the Incorporation of Brain-computer Interfaces and Artificial Intelligence. BioEng BioElectron. 05:04.
Copyright: © 2023 Mathew J. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
