Brain–computer interface
A brain–computer interface (BCI), sometimes called a brain–machine interface (BMI), is a direct communication link 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 of moving body parts (e.g. hands or feet). BCI implementations range from non-invasive (EEG, MEG, MRI) and partially invasive (ECoG and endovascular) to invasive (microelectrode array), based on how physically close electrodes are 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 the Defense Advanced Research Projects Agency (DARPA). Vidal's 1973 paper introduced the expression brain–computer interface into 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 were implanted in humans in the mid-1990s.
Beyond their classification by physical invasiveness, brain computer interfaces (BCIs) are also often classified by the way they function and by their intended application. One distinction, which is widely used, separates active and passive BCIs. Active BCIs require that users consciously modulate their neural activity e.g., through the application of motor imagery or mental arithmetic or through the application of focused attention in order to provide commands to an external system. These systems are utilized to translate intentional neural patterns and are used to form control signals, and are often associated with applications requiring direct user input, e.g., to control a cursor, how to spell words or to operate a robotic device.
In contrast, with passive BCIs there is no control of the user's intention. Instead, they monitor ongoing brain states continuously; they can be used to monitor levels of mental workload, alertness, fatigue or affect, and this information can be used to let computing systems adapt themselves to the user's cognitive or emotional state on the fly. Passive BCIs are often built into adaptive systems that manipulate the difficulty of a task, presentation of information or behaviour of the system without having to give explicit commands from the user. Some authors also go on to identify reactive BCIs, which recognize neural responses elicited by external stimuli such as event-related potentials rather than by voluntary mental actions. These systems lie in between the active and passive, in that they rely on stimulus-driven brain responses but also provide real-time intentional interaction.
BCIs may also be classified based on the main field of application. Clinical and neuroprosthetic BCIs are widely developed to restore or compensate for lost motor or sensory abilities, for example as communication assistance to persons with severe neuromuscular impairments and as means of controlling prosthetic limbs or assistive devices. In research settings, the BCIs are being used as experimental tools in cognitive neuroscience and neurophysiology where they are supporting the study of brain function, learning and neural plasticity. Such systems enable researchers to see how neural signals are changed in response to feedback/training/environmental demands.
More recently, especially with the development of non-invasive approaches to signal acquisition, BCIs have been studied as part of more general human-computer interaction. These range from adaptive user interfaces to virtual and augmented reality environments, gaming and entertainment systems where BCIs can be used as a complement to, instead of replacing or augmenting traditional input techniques. Across these application areas different forms of BCIs involve trade-offs in terms of signal quality, reliability, ease of use and practicality. Systems that are based on deliberate user control often focus on accuracy and system responsiveness, while passive systems aim at constant monitoring and minimum user effort .