Brain-computer interface (BCI) technology is a growing field, which is estimated to be worth more than $1 billion globally. Frost & Sullivan research indicates that the health care segment occupies the largest share (52%) of this market and is expected to draw the highest demand in coming years. Read about some of the latest advances.
Brain-computer interface (BCI) technology is a growing field of interest with medical applications ranging from prevention, detection, and diagnosis to rehabilitation and restoration. A BCI is a combination of hardware and software communications that allow humans to control external devices such as computers through cerebral activity alone. Extensive BCI research is being done to develop devices that aid people with disabilities, particularly those affected by neurological and neuromuscular conditions such as spinal cord injury, brain strokes, and amyotrophic lateral sclerosis.
Commonly Used BCI Technologies
Electroencephalography (EEG), which records electrical activity directly from the brain through electrodes placed on the scalp, remains the top neuroimaging method for BCI-based products because it is extremely cost-effective and portable. Magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) have shown promise in BCI research due to their non-invasive characteristics and good temporal and spatial resolution. MEG uses magnetometers to capture the magnetic fields produced by the brain’s electrical activity; fMRI uses MRI technology to detect changes in the brain’s blood flow.
Some of the most promising BCI application areas are explained below.
Disabilities affecting locomotion degrade quality of life and reduce life expectancy. Scientists have been considering invasive and non-invasive approaches to help people who use wheelchairs.
The non-invasive approach is preferred because of its ease of use and limited or no discomfort. Paraplegics or those with other locomotor disabilities can drive wheelchairs using EEG-enabled BCI devices. Users can activate three levels of assistance with a BCI-powered wheelchair: obstacle avoidance, collision avoidance and orientation recovery. Laser scanners on the wheelchair enable a degree of autonomy by detecting potential obstacles, allowing the device to accordingly evaluate the situation. The main challenge with these BCI-powered wheelchairs is the lack of accurate, real-time control because of infrequent signals and low information transfer rate.
As a result, research continues in the development of invasive devices that are driven by user decisions and overcome the commonly faced low bit rate of non-invasive BCIs. Invasive BCI systems include electrodes that are implanted in the brain. These solutions have yet to evolve completely because current offerings can be painful and require regulatory approval before use. Proof of efficacy requires extensive clinical trials that are costly and time-consuming, which can discourage some manufacturers. The University of Twente in the Netherlands is among the academic institutions exploring the use of BCI systems in wheelchair mobility. Similar research in prosthesis and environment control is continuing at the University of California with the goal of helping paraplegics regain basic brain-controlled motion.
Motor Restoration to Treat Several Neurological Disorders
Evaluation of brain signals through EEG recording can help treat spinal cord injuries and neurological disorders such as migraines and cluster headaches, and can aid in neuroprosthetics.
Restoring the Sense of Touch
Researchers from the University of Chicago have developed a BCI solution that could restore the sense of touch for paralyzed patients. The solution consists of a robotic arm that is connected to the user’s brain via BCI technology. This solution is based on biomimetic mapping-based BCIs that aim to capture the natural relationship between cortical activity and volitional arm or hand movement that is then used to control a prosthetic arm or orthosis. The robotic arm provides a sensory feedback based on the patterns recorded in the system transmitted from the electrodes that are implanted in the brain of the user. The FDA is in the process of approving similar devices for human trials.
Speech Recognition from Neural Networks
Speech recognition technologies are among the most widely adopted across many industries. Researchers from the University of Bremen, Germany, are exploring the development of a BCI solution that can understand neural signals suitable for automatic speech recognition (ASR) to help people with speech impairments. EEG, fMRI, MEG and near infrared spectroscopy are being investigated as enabling technologies. The concept is still in the early stages of research. Research has been demonstrated with epilepsy patients, and the capability of decoding brain signals for ASR accurately was validated.
Artificial Neural Networks Using Memristors
Researchers from the University of Southampton in the United Kingdom are developing a computational system to mimic the human brain. The low-power, nanoscale memristor device could be integrated into prosthetics and implants to detect neural signals and produce movement. The research is still in its early stages.
What’s the Future?
The BCI market, which is estimated to be worth more than $1 billion globally, includes health care, entertainment and gaming, neuromarketing and environment control. Frost & Sullivan research indicates that the health care segment occupies the largest share (52%) of this market and is expected to draw the highest demand in coming years. Advancements in prosthetics and implantable electronics are expected to complement BCI developments. Patent filings in the areas of neuroprosthetics, rehabilitation, epilepsy treatment and robotics have been extremely promising in the last five years, but classification of any BCI-based device as medical therapy requires regulatory approval. Manufacturers’ willingness to invest in clinical trials will be crucial for success.
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