CURRENT TRENDS IN BRAIN COMPUTER INTERFACE (BCI)
Trends in Brain computer interface
BRAIN COMPUTER INTERFACE (BCI) comprises of three different novel aspects of BCI technology.
- Development of a non-invasive mind controlled robotic arm: Carnegie Mellon University and University of Minnesota (January 2020)
- Development of automated brain sourcing :University of Helsinki, Finland (May 2020)
- Creating of artificial synapse that works with living cells: Stanford University (June 2020)
Let’s see the details of the current trends in BCI research!!!!
1) Development of a non-invasive mind controlled robotic arm:
One of the most rapidly evolving technologies is Brain Controlled Interface (BCI) with an enhanced clinical growth of systems so that there could be an independent interaction between the patients suffering from neurological disorders and their respective environment. In the past, techniques utilized signals obtained from chips which are fit in the brain for the purpose of robotic device control. However to operate these brain implants, a good amount of medical and surgical expertise is required so that is can be incorporated to a few clinical nodes. Therefore to address the problem a non-invasive robotic device control to track in an incessant manner by means of following a computer cursor was done by researchers from Carnegie Mellon University and University of Minnesota.
The Future of BCI technology:
In between the brain and external device, the BCI technology sends and receives the signals. With the help of EEGs, the researchers tracked and acquired the signals deep within the brain in a non-invasive manner and therefore the neural control of a robotic arm was achieved. With the help of fixed target location and a discrete target paradigm, a continuous pursuit task which involves the track mode of the target was done. This technology was tested on 68 subjects where each subject activity took part upto ten 10 session thereby the cursor was able to capture the thoughts accurately. In earlier cases, there was a huge amount of error in the human controlled robotic arms motions. But this new technology allows the arm to track the cursor in a continuous mode so that the accurate tracking is done by over 500%. The learning effects which involved both behavioral and psychological aspects were produced by the continuous pursuit task. The poor signal concerns were overcome by the researchers that are usually caused with spatial filtering in typical non-invasive systems which includes EEGs. The non-invasive mode control of such robotic device progresses with thoughts so that a variety of applications with huge benefits is given to patients with movement disorders. Future clinical trials too are in progress.
Figure 1 Non-invasive Mind controlling Procedure
2) Automated brain sourcing:
Human preferences identified by brain sourcing automatically too are easily done. With the help of Artificial Intelligence (AI), researchers have developed a technique in order to analyze opinions and to conclude on the brain activity of certain people. The technology is identified as brain sourcing and is utilized to classify images and to recommend content which is not done before. Artificial Intelligence helps to monitor EEGs so that a large group of people can be determined from the brain activity. To break a very complex task into simpler task, crowd sourcing is used so that a large number of people could utilize it for distributions and can be solved individually. The responses which people give can be stored as instructional data in an image/signal recognition system. Fully automated systems on AI are still not available and several people should consent to give the sample images for training. The help of AI techniques in analyzing people’s EEG proved to be a great benefit. By utilizing the natural reactions of people, crowd sourcing can be incorporated to image recognition in order to carry out the manual tasks with the help of a mouse or keyboard. For the image classification of computers, a total of 30 volunteers were given the display of human facial images. Labeling the faces by the participants to read the mindset was mandatory. A person was characterized by facial features, hair colour, smiling or not smiling feature etc. No additional information was provided to the mouse or keyboard like in the traditional crowd sourcing tasks. The EEG was used to take the brain activity of each participant. The AI algorithm was used to recognize the images which are pertinent to the task. The mental labels were interpreted directly from the EEG in the results of the experiment. Thus to be very simple and well maintained recognition tasks, the application of brain sourcing can be done. With the help of data collected from 12 volunteers, the highly reliable labeling results were obtained. It is one of the most user friendly methods in the way. It is actually a combination of brain and computer activity that is found in various interfaces of the mode.
Figure 2 Automated Brain Sourcing
3) Creating of artificial synapse that works with living cells:
For mimicking the brains coefficient learning process, Stanford University analyzed the creation of artificial synapse. It is actually an artificial version of a synapse and the researchers proved that they could be parallel operated so that the mimicking is done simultaneously along with the operation of the brain. The special strength of the materials used to interact with living matter is highlighted in this work. On the soft polymers, the cells are happily sitting and the compatibility goes much deeper. The same molecules which are used naturally by the neurons are used to work with these materials too. To process the brain’s message, an electrical signal is required by the brain-integrated devices and the communication between the living cells and the devices is extraordinary. A trench is used to separate the electrolyte solution and the polymer electrodes that help to communicate with the neurons. The ions can travel to the trench and so the conductive state of the electrode is modulated. The learning process and its respective simulation is preserved in nature. A synaptic junction is used to control the chemical interactions of a biological synapse. The mimicking is done by the process were some kind of learning is done so that the memory and the capacity aspect of action potentials are stored. The data is initially processed and so it is applied in traditional computer systems too. Rat endocrine neurocells was used for testing that release the dopamine neurotransmitter. The effect of dopamine was well analyzed as they saw a permanent change in the state of mind in the device utilized. Thus a bio-hybrid design that can demonstrate the communication melding is possible. Now the researchers have successfully implemented and tested the design, their future work is to test the device function work in a more versatile manner by means of setting more complicated biological settings to differentiate various cells and neurotransmitters.
Figure 3 Creating of a biohybrid artificial synapse