Brain-Computer Interface for Cognitive Augmentation
Chris Hope, EEG Recording Cap, CC BY 2.0
With the increase in computational power and networking speed, there is a rise in dependency of humans over Artificial Intelligent (AI) systems. The AI systems will soon surpass the human brain and its potential and capability will exponentially accelerate in an uncontrollable incoherent way. In order to work synchronously with artificial intelligence, reverse engineering is required which will help humans to empower and augment their own brain. The brain-to-machine (B2M) interface will keep up the pace of human intelligence with machine intelligence and will play a major role in complementing and enhancing decision making by humans. This augmentation will play a major role in education and training as the learners will be able to monitor and track the thought-process of the human brain. This brain-machine interface will provide the learners with the relevant stimulation to enhance their memory, knowledge and the cognitive skills.
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The brain augmentation can be achieved by interfacing the brain with the computer just like ‘telepathy’. It can also be called as connecting the brain with machine through ‘Bluetooth’. The bi-directional information flow between brain and machine using non-invasive wearable technology can bring augmented human intelligence.
Figure 1, BCI Systems. Adapted from Nijholt, A., & Tan, D. (2008). Brain-computer interfacing for intelligent systems. IEEE Intelligent Systems, 23(3), 72-79. doi:10.1109/MIS.2008.41
Non-invasive/mobile brain-computer Interface takes input signals from brain using various forms such as magnetoencephalograms(MEG), electroencephalograms(EEG), blood-oxygen-level-dependent signals(BOLD) and (de)oxyhemoglobin concentrations. These patterns of signals are digitized and preprocessed by the signal processing system of BCI. The signal (data inputs) are then filtered by feature selection and extraction algorithms which are further fed to the classification and machine learning algorithms. These algorithms infer the user's state of mind and convert it into commands for the application software. The software takes the commands, interprets and performs the desired output which is fed back to the user to augment the decision making and cognitive skills of the user. This neuro decoding and encoding have led to brain-machine interface which optimizes the brain stimulation to achieve the desired goals.
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There are various types of BCI systems:
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Invasive BCI involves use of electrodes which will be implanted on or inside of the cortex of the brain.
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Non-invasive BCI uses EEG sensors as multiple electrodes. These electrodes are attached to the scalp of a person which analyses the brain wave activity by measuring the voltage fluctuations caused by ionic current within the neurons.
Wearable/Mobile Brain-Computer Interface used in Learning
BCI systems used in increasing the task-performance during training
There is ongoing research on the application of BCI systems for enhancing the capability and cognitive skills of learners in education and training. The wearable/mobile BCI systems are being developed which will be used to enhance the memory retention, alertness and concentration of learners for boundary-avoidance tasks (high risk and high demanding tasks). In order to enhance the sensory motor skills of the users and increase the task performance, a research has been done on how by using the EEG information collected by BCI systems can help in regulating the arousal of the human brain by creating the closed neuro-feedback loop. Using Yerker-Dodson law, the arousal is shifted from right-side of the curve to optimal level by using online veridical neurofeedback which enhances the performance for high-demanding tasks and decreases the probability of failures.
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Yerkes and Dodson Law
Brain co-processors used in inducing the plasticity and augmenting the brain functionality
Brain co-processors will play a major role in stimulating the certain parts of the brain and increasing the memory formation and recall which will help the learners to quickly learn the skills. Furthermore, sensors and neurostimulators of brain co-processors also called Computer-Brain Interface will be able to track the student's progress, their pace of learning and their mental alertness. With the closed-loop stimulation the brain-coprocessor can induce plasticity which will accelerate the knowledge acquisition and learning capability of learners.
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Companies doing research on Brain-Computer Interface
NextMind’s noninvasive brain-computer interface debuted at CES 2020. NextMind
There are a number of companies which are working on the BCI technologies and the various ways they can interact with humans.
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Neuralink is trying to embed a tiny array of electrodes (typically 10 arrays each having 100 electrodes) directly in the brain of humans. The electrodes will be implanted using laser surgery by robots in order to minimize the piercing of micro vessels.
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Paradromics is also using an array of electrodes which will be more in number and density than Neuralink but shorter in size for brain-machine interface.
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Synchron's technology inserts the BCI by using a stent through the vein in the back of the neck and places the stent next to the motor cortex of the brain in order to avoid open-surgery.
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Facebook is also doing research in developing non-invasive wearable BCI which can help in the augmentation and direct interaction of the brain with the machines.
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NextMind, a neurotechnology company is also doing groundbreaking research in non-invasive brain-computer interface.
Ethical Considerations
There are numerous advantages of BCI systems which will bring the extension to human capabilities. The mobile/wearable BCI technology will affect the learning and memory extension of the human brain tremendously. However, it is important that we discuss the ethical implications which comes with every technology. With the research and development of mobile/wearable BCI systems, we need to address the concerns despite the advantages of BCI systems in empowering human intelligence. The use of BCI systems raises a question on humanity and personhood. Technology becoming part of the user's body-schema may change the identity of the person with respect to personhood, social identity, authenticity, character and psychology. Furthermore, use of BCI systems may affect the autonomy of an individual. The decisions, consent and privacy of an individual may be controlled by BCI systems where individuals may have less control over their thoughts. In addition to this, whether the responsibility and accountability of unintended actions taken using this assistive technology will be either on the user or BCI manufacturer is currently a debatable topic; as these actions may come with the liability. The issue of privacy and security may also be another concern among ethical researchers as personal information and thoughts could be extracted by hacking brain-computer interfaces. Besides the moral and ethical considerations, the physical/medical safety and well-being with invasive BCI technology will be an overarching concern. Therefore, the research community, along with the regulatory agencies, should develop an ethical framework to guide scientists in development of this technology which will help in the use of BCI systems in constructive and productive human augmentation.
Bibliography:
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Nijholt, A., & Tan, D. (2008). Brain-computer interfacing for intelligent systems. IEEE Intelligent Systems, 23(3), 72-79. doi:10.1109/MIS.2008.41
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Faller, J., Cummings, J., Saproo, S., & Sajda, P. (2019). Regulation of arousal via online neurofeedback improves human performance in a demanding sensory-motor task. Proceedings of the National Academy of Sciences - PNAS, 116(13), 6482-6490. doi:10.1073/pnas.1817207116
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Burwell, S., Sample, M., & Racine, E. (2017). Ethical aspects of brain computer interfaces: A scoping review. BMC Medical Ethics, 18(1), 60. doi:10.1186/s12910-017-0220-y
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Rao, R. P. N. (2020). Brain co-processors: Using AI to restore and augment brain function.
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