A brain-computer interface (BCI), also sometimes referred to as a brain-machine interface (BMI), is a communication and/or control system that allows real-time interaction between the human brain and external devices. A BCI user’s intent, as reflected by brain signals, is translated by the BCI system into a desired output: computer – based communication or control of an external device.
Types of Brain Signals
In principle, a variety of neurophysiologic signals reflecting in-vivo brain activities might be recorded and used to drive a BCI. Depending on the biophysical nature of the signal source, these signals can be broadly grouped into three categories: electrophysiological, magnetic, and metabolic.
Magnetic and Metabolic Signals
Magnetoencephalography (MEG) was recently proposedMusaa potential new source of brain-derived signals to operate a BCI. MEG is attractive because it is non-invasive, able to detect frequency ranges above those available in EEG recordings, and has slightly higher spatial resolution than EEG.
MEG measures very small magnetic fields produced by the electrical activity of the brain. Researchers have explored the potential of MEG BCI in the rehabilitation of stroke patients with encouraging initial results. Recent BCI research using MEG signals demonstrated reliable self-control of sensorimotor rhythm amplitude and satisfactory two-dimensional BCI control. Despite its wider frequency range and excellent spatiotemporal resolution, MEG currently requires bulky and expensive equipment and a protected environment, and thus is at present impractical for widespread clinical use.
Signal acquisition is the measurement of the neurophysiologic state of the brain. In BCI operation, the recording interface (i.e., electrodes, for electrophysiological BCI systems) tracks neural information reflecting a person’s intent embedded in the ongoing brain activity. As discussed in the last section, the most common electrophysiological signals employed for BCI systems include: EEG recorded by electrodes on the scalp; ECoG recorded by electrodes placed beneath the skull and over the cortical surface; and local field potentials (LFPs) and neuronal action potentials (spikes) recorded by microelectrodes within brain tissue. The brain electrical signals used for BCI operation are acquired by the electrodes, amplified, and digitized.
The signal-processing stage of BCI operation occurs in two steps. The first step, feature extraction, extracts signal features that encode the intent of user. In order to have effective BCI operation, the electrophysiological features extracted should have strong correlations with the user’s intent. The signal features extracted can be in the time-domain or the frequency-domain or both. The most common signal features used in current BCI systems include: amplitudes or latencies of event-evoked potentials (e.g., P300), frequency power spectra (e.g., sensorimotor rhythms), or firing rates of individual cortical neurons. An algorithm filters the digitized data and extracts the features that will be used to control the BCI. In this step, confounding artifacts (such as 60-Hz noise or EMG activity) are removed to ensure accurate measurement of the brain signal features.
The second step of signal processing is accomplished by the translation algorithm, which converts the extracted signal features into device commands. Brain electrophysiological features or parameters are translated into commands that will produce output such as letter selection, cursor movement, control of a robot arm, or operation of another assistive device. A translation algorithm must be dynamic to accommodate and adapt to the continuing changes of the signal features and to ensure that the possible range of the specific signal features from the user covers the full range of device control.
The operating protocol determines the interactive functioning of the BCI system. It defines the onset/offset control, the details of and sequence of steps in the operation of the BCI, and the timing of BCI operation. It defines the feedback parameters and settings, and possibly also any switching between different device outputs. An effective operating protocol allows a BCI system to be flexible, serving the specific needs of an individual user. At present, since most BCI studies occur in laboratories under controlled conditions, investigators typically control most of the parameters in the protocol, providing simple and limited functionality to the BCI user. More flexible and complete operating protocols will be important for BCI use in real life, outside of the laboratory.
BCI Clinical Applications
Potential BCI Users
Individuals who are severely disabled by disorders such as ALS, cerebral palsy, brainstem stroke, spinal cord injuries, muscular dystrophies, or chronic peripheral neuropathies might benefit from BCIs.
Communication for people who are “locked in” probably represents the most pressing area in need of intervention with BCI technology. Although other applications are under development, restoring communication has been the main focus of the BCI research community to date.
Restoration of motor control in paralyzed patients is another key application of BCI and is the main goal of many researchers in the field. The research in this clinical application is sparse and has used mainly SMR-based systems.
BCI-based environmental control could greatly improve the quality of life of severely disabled people. People with severe motor disabilities are often home-bound. Effective means for controlling their environments (e.g., controlling room temperature, light, power beds, TV, etc.)
Aminu Ibrahim Musa