Brain-computer interfaces: Definitions and principles

Throughout life, the central nervous system (CNS) interacts with the world and with the body by activating muscles and excreting hormones. In contrast, brain-computer interfaces (BCIs) quantify CNS activity and translate it into new artificial outputs that replace, restore, enhance, supplement, or improve the natural CNS outputs. BCIs thereby modify the interactions between the CNS and the environment. Unlike the natural CNS outputs that come from spinal and brainstem motoneurons, BCI outputs come from brain signals that represent activity in other CNS areas, such as the sensorimotor cortex. If BCIs are to be useful for important communication and control tasks in real life, the CNS must control these brain signals nearly as reliably and accurately as it controls spinal motoneurons. To do this, they might, for example, need to incorporate software that mimics the function of the subcortical and spinal mechanisms that participate in normal movement control. The realization of high reliability and accuracy is perhaps the most difficult and critical challenge now facing BCI research and development.

The ongoing adaptive modifications that maintain effective natural CNS outputs take place primarily in the CNS. The adaptive modifications that maintain effective BCI outputs can also take place in the BCI. This means that the BCI operation depends on the effective collaboration of two adaptive controllers, the CNS and the BCI. Realization of this second adaptive controller, the BCI, and management of its interactions with concurrent adaptations in the CNS comprise another complex and critical challenge for BCI development.

BCIs can use different kinds of brain signals recorded in different ways from different brain areas. Decisions about which signals recorded in which ways from which brain areas should be selected for which applications are empirical questions that can only be properly answered by experiments.

BCIs, like other communication and control technologies, often face artifacts that contaminate or imitate their chosen signals. Noninvasive BCIs (e.g., EEG- or fNIRS-based) need to take special care to avoid interpreting nonbrain signals (e.g., cranial EMG) as brain signals. This typically requires comprehensive topographical and spectral evaluations.

In theory, the outputs of BCIs can select a goal or control a process. In the future, the most effective BCIs will probably be those that combine goal selection and process control so as to distribute control between the BCI and the application in a fashion suited to the current action. Through such distribution, BCIs may most effectively imitate natural CNS operation.

The primary measure of BCI development is the extent to which BCI systems benefit people with neuromuscular disorders. Thus, BCI clinical evaluation, validation, and dissemination is a key step. It is at the same time a complex and difficult process that depends on multidisciplinary collaboration and management of the demanding requirements of clinical studies.

Twenty-five years ago, BCI research was an esoteric endeavor pursued in only a few isolated laboratories. It is now a steadily growing field that engages many hundreds of scientists, engineers, and clinicians throughout the world in an increasingly interconnected community that is addressing the key issues and pursuing the high potential of BCI technology.