Brain-computer interfaces (BCIs) represent a rapidly advancing realm of technology with the potential to transform our interactions with computers and our environment. BCIs enable individuals to communicate and manipulate devices through their brain activity, eliminating the need for physical muscle or nerve involvement.

BCIs operate by recording and interpreting brain signals, employing various techniques such as electroencephalography (EEG), magnetoencephalography (MEG), and intracortical recordings. EEG stands out as the most prevalent BCI method due to its non-invasiveness, involving the placement of electrodes on the scalp. MEG, while also non-invasive, demands specialized and costly equipment. Intracortical recordings, although the most invasive option, yield the highest quality brain signals.

Following the acquisition of brain signals, machine learning algorithms are employed for decoding. These algorithms are trained to correlate distinct brain patterns with specific commands. For instance, a BCI can be trained to recognize brain patterns associated with the intention to move a cursor left or right. Once trained, users can manipulate the cursor simply by thinking about the desired movement.

The potential applications of BCIs are wide-ranging. One of the most promising applications is assisting individuals with disabilities. BCIs could empower individuals with paralysis to communicate, control computers, and even manage prosthetic limbs. Additionally, BCIs have the potential to aid those with conditions such as stroke, cerebral palsy, and amyotrophic lateral sclerosis (ALS).

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