Interface, interaction, and intelligence in generalized brain–computer interfaces

Classical brain–computer interface (BCI) systems are moving beyond lab demonstrations to real world applications with the development of associated hardware and software. Brain–computer interaction systems open up a wide range of BCI applications, especially in neural rehabilitation and human cognitive augmentation. Brain–computer intelligence systems reveal promising technologies for a new generation of artificial intelligence (AI) as well as a new generation of BCIs. A brain–computer interface (BCI) establishes a direct communication channel between a brain and an external device. With recent advances in neurotechnology and artificial intelligence (AI), the brain signals in BCI communication have been advanced from sensation and perception to higher-level cognition activities. While the field of BCI has grown rapidly in the past decades, the core technologies and innovative ideas behind seemingly unrelated BCI systems have never been summarized from an evolutionary point of view. Here, we review various BCI paradigms and present an evolutionary model of generalized BCI technology which comprises three stages: interface, interaction, and intelligence (I3). We also highlight challenges, opportunities, and future perspectives in the development of new BCI technology. A brain–computer interface (BCI) establishes a direct communication channel between a brain and an external device. With recent advances in neurotechnology and artificial intelligence (AI), the brain signals in BCI communication have been advanced from sensation and perception to higher-level cognition activities. While the field of BCI has grown rapidly in the past decades, the core technologies and innovative ideas behind seemingly unrelated BCI systems have never been summarized from an evolutionary point of view. Here, we review various BCI paradigms and present an evolutionary model of generalized BCI technology which comprises three stages: interface, interaction, and intelligence (I3). We also highlight challenges, opportunities, and future perspectives in the development of new BCI technology. extend BCI applications from the current laboratory or clinical environment to real daily life by enabling them to function when individuals interact with the environment. real-time BCI systems in which the brain and external devices bidirectionally interact with each other. directly decodes higher-order, goal-oriented cognitive signals to send intuitive BCI commands without goal-irrelevant and indirect thinking. uses flexible, closely spaced subdural grid or strip electrodes that are placed directly on surgically exposed brain surface to measure cortical electrical activity. This technique is characterized by high spatio-temporal resolution, broader bandwidth, and excellent signal-to-noise ratios (SNRs). utilizes electrodes that are placed on the scalp surface to non-invasively measure electrical potentials that arise from activity in the brain. EEG primarily reflects the sum of post-synaptic potentials from cortical neurons. an electrophysiological brain signal that is time-locked to the occurrence of an event. Typically, the latency and amplitude of ERP can be obtained by averaging multiple trials in the time domain. an electrical potential that is caused by the nervous system in response to a sensory stimulus. Various stimuli may generate evoked potentials, but visual, auditory, and somatosensory are the most frequently used stimulus types. utilizes magnetic resonance imaging to noninvasively measure changes in the blood oxygenation level dependent (BOLD) signal as indication for local brain activity. calculates the concentration changes of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) in a brain tissue based on the changes of the exiting-photon intensity and the incident-photon intensity, and then characterizes the local neural activity. any system that has direct interaction between the brain and external devices. combines a BCI with another system(s) that utilize other physiological or technical signals. The purpose is to integrate diverse input signals to achieve better BCI performance. a noninvasive imaging technique that utilizes a superconducting quantum interference device (SQUID) to measure extremely weak magnetic fields outside the head. MEG can directly reflect the magnetic field changes caused by cortical neural activity on a millisecond timescale. an interface that derives its output from naturally occurring brain activity during task execution to act as a complementary input providing information about ongoing user mental states (e.g., workload, emotional state, or attention levels). a BCI system based on P300 event-related potential that is a positive deflection at approximately 300 ms after a rare and relevant stimulus. P300 signals can be increased in amplitude when the particular stimulus is given greater attention. a BCI system based on mu (8–12 Hz) and beta (18–26 Hz) oscillations in EEG signals recorded over sensorimotor cortex. The amplitudes of SMRs can be modulated using mental strategy of motor imagery. a BCI system based on very slow variation of the cortical activity. Positive SCPs correlate with mental inhibition and relaxation, whereas negative SCPs coincide with mental preparation. a BCI system based on periodic brain responses induced by repeated visual stimulation. SSVEPs appear as an increase in brain activity at the stimulation frequency and its harmonics.