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How Does Brain-Computer Interface Work

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Brain-computer interfaces serve as the conduit between the brain and external devices, such as computers, enabling the substitution or enhancement of human peripheral capabilities without requiring muscular movements. They offer significant freedom across various domains, including gaming, neuroscience, rehabilitation, robotics, and affective computing.

Unlike traditional neural pathways reliant on neurotransmitters at synapses to transmit information from the Central Nervous System to the Peripheral Nervous System, BCIs operate independently of chemical processes. Instead, they analyze, interpret, and translate signals into commands that are relayed to output devices to execute desired functions.

The concept of Brain-Computer Interfaces originated as a potential solution for neural rehabilitation and direct control of assistive devices by the brain. In 1973, J.J. Vidal pioneered practical implementation of BCI technology by utilizing Electroencephalogram to record electrical activity and signals from the cerebral cortex through an intact skull.

How Does BCI Work

BCI operates by either re-exciting or artificially enhancing neural plasticity within dysfunctional neural circuits, leveraging intact emotional and cognitive functions to restore the connection between peripheral sites and the brain.

Constructing a Brain-Computer Interface necessitates five to six components: signal acquisition during experimental paradigms, pre-processing, feature extraction (e.g., SSVEP, P300 amplitude, alpha/beta bands), detection (classification), translation of classifications for commands (BCI application), and user feedback. Various software tools have been developed for rapid analysis and processing, including EEGLab, BCI2000, FieldTrip, and Brainstorm. These software solutions rely on AI algorithms and advanced image and signal processing techniques for source/sensor level analysis.

Signal Acquisition:

The first and essential component of the BCI system is signal acquisition, which involves measuring brain signals using sensory modalities such as scalp electrodes, fMRI for metabolic activity, and intracranial electrodes to measure physiological activity. Once acquired, the signals are amplified to a level suitable for further processing. Additionally, any extraneous noise is filtered out from these signals during amplification. Following the filtering process, the signals are transmitted to the computer for digitalization.

Feature Extraction:

Pertinent signal characteristics, also known as desired signals, are extracted in this procedure to distinguish them from undesired signals through computer analysis. These extracted characteristics are then transformed into a compact form that can be feasibly translated into output commands. It’s crucial for these features to correlate with the user’s intent. Commonly extracted signal characteristics include firing rates of cortical neurons, time-triggered ECoG response latencies and amplitude, and power within ECoG or EEG. To ensure accuracy, physiological artifacts such as electromyographic signals and environmental artifacts like noise are filtered out and avoided.

Feature Translation:

Extracted features undergo feature translation algorithms that convert these features into commands for output devices. For example, a decrease in power in a frequency band might result in the upward displacement of the cursor on the computer. Similarly, the P300 potential can be translated for selecting the letter that evoked it. The translation algorithm should be dynamic to adapt to learned or spontaneous changes in signal features. This ensures that a possible range of features covers the entire range of device controls.

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Device Output:

Commands translated from extracted features control and operate the external device, whether through cursor movement, letter selection, robotic arm operation, or other means. Therefore, the device output controls the user’s feedback, completing this BCI loop.

Types of BCIs

There are two types of BCIs based on electrode usage: invasive and non-invasive.

In invasive BCI, electrodes are applied directly to the brain through surgeries, such as electrocorticography (ECoG) and intracranial electroencephalography (iEEG). In contrast, non-invasive BCI involves applying electrodes to the scalp, as seen in EEG-based BCI.

Non-invasive methods are considered safer than invasive ones because invasive procedures require surgeries, which may impact the psychology of the individual and increase the risk of infectious diseases.

BCI Applications in Different Fields

Brain-Computer Interface and Aging:

Aging is an inevitable process that everyone experiences, leading to a decline in both brain and body functions. Even those who maintain good health in old age may experience a decrease in brain function and memory. Additionally, physiological changes such as brain parenchyma shrinkage and reduced blood flow to the brain can impact neurotransmitter function. These changes may contribute to feelings of anxiety or depression, increasing dependence on others.

This is where BCI comes into play, offering assistance to aged and disabled individuals by reducing their reliance on others. BCI serves as an adaptive, assistive, and rehabilitative technology that reads or interprets brain activity in older adults and translates it into signals that can operate devices.

BCI can be helpful for older adults in different ways like:

  • Training their cognitive and motor activities to delay the aging effects.
  • Controlling home appliances for routine work.
  • Communicating with others just by thinking, without the movement of the peripheral system.
  • Controlling exoskeleton to enhance the strength of muscles.

The brain-computer interface translates brain activity signals into commands for external devices by directly sending neural responses.

BCI is equally beneficial for aged and disabled individuals, enhancing their quality of life by assisting with daily tasks, facilitating communication and relationship-building with family, and promoting self-sufficiency. BCI can effectively aid in restoring learning and improving attention, memory, and consciousness in elderly patients experiencing cognitive impairment.

Brain-Computer Interface and Medicine:

Despite intact cognition, many neurological disorders impair voluntary movements, motor functions, and communication. Over the last two decades, multiple BCI approaches have been developed based on slow waves, EPs (Evoked Potentials), SSEPs (steady-state evoked potentials), and MI paradigms (Motor imagery) to advance the medical field through BCI. The first BCI applications were used to control prosthetics, spell, and manipulate computer cursors.

The role of BCI is widespread in medicine, being utilized in automated wheelchair control through the BCI approach. Another exclusive clinical application of BCI is facilitating motor function after spinal cord surgery or stroke. BCIs for rehabilitation are being implemented alongside conventional assistive devices like transcranial direct current stimulation (tDCS) and functional electrical stimulation (FES-based) neuro-prostheses to enhance the brain’s ability to recognize cortico-muscular and corticospinal connections after sub-acute, acute, or chronic lesions.

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BCI-induced brain plasticity is also effective in treating high-order cortical dysfunction for improving emotional and social behavior in autism spectrum disorder and rehabilitating cognitive disorders related to dementia. Another exciting application of BCI is detecting inner speech without any external speech stimulation.

BCIs can be helpful in diagnosing certain brain disorders. They can detect neural signals of cognitive processes in patients diagnosed with disorders of consciousness (DOC). BCIs play a role in improving visual assessment in glaucoma, providing real-time brain mapping for multiple neurosurgeries, detecting intra-operative assessment during anesthesia, and screening cognitive functions in cases of complete immobility. For restoring mobility in motor impairment patients, the invasive method of intra-cortical recording is better than non-invasive methods.

Affective Gaming, Robotics, Computing, and other Miscellaneous Applications:

Advanced computers in the future are expected to possess perceptual and emotional capabilities, aiding humans in decision-making. Recent research suggests that BCI is a potent tool for investigating different affective states and expanding clinical applications into psychology. EEG-based BCI can detect and interpret negative and positive emotions induced by video stimuli.

Artistic BCI integrates arts into BCI. David Rosenboom was the first to experiment with linking brain functions with musical production, proprioception perception, and musical forms. Other examples of Artistic BCI include video gaming, affective state detection, and controlling virtual/augmented reality environments. Users can fully control and enjoy video games through SSVEP-BCI. Multiple users can also participate in games requiring joint decision-making to control the game. Moreover, modifications in the BCI setup can investigate how people interact in diversified social contexts, extending applications to sociology.

Virtual/Augmented Reality (V/AR) technologies and BCI have numerous arts and neurofeedback implications. Brain painting enables drawing on canvas without moving hands, facilitating communication for people with paretic motor impairment.

BCI-driven robotic controllers offer advanced assistive technology to individuals with mobility constraints. EEG-based mobile robots or wheelchairs have demonstrated success. Additionally, robots are utilized in hazardous environments, such as BCI-controlled robots in coal mining. BCI robots are also deployed to enhance astronauts’ working capacity, safety, efficiency, and functionality.

Challenges and Concerns related to BCI

Undoubtedly, the inventions and breakthroughs of Brain-Computer Interface across various fields of medicine, engineering, computing, and space are astonishing. However, there are social and ethical concerns regarding the application of BCI. Factors such as user safety, privacy protection, ethics, community acceptance, data confidentiality, and socioeconomic aspects are pertinent to ensuring user satisfaction. In some instances, users’ physical and mental safety may be compromised; invasive procedures like intracranial microelectrodes and deep brain stimulation may lead to post-operative neurological and psychological effects. Implanted microelectrodes have the potential to alter emotions, thoughts, feelings, personality, and memories, necessitating strict guidelines and care to ensure safety and ethical measures.

Conclusion

We can conclude that the future belongs to Brain-Computer Interface if it is executed efficiently. Brain-Computer Interface is an advanced technology that could uplift various fields such as technology, medicine, and many others if used with positive and constructive thinking. It offers countless benefits, including reducing the lag between moving the mouse and cursor, which is particularly useful in military and gaming applications. The clinical applications of BCIs in medicine are tremendous, making the lives of paralyzed individuals easier and reducing dependence on others. Additionally, fields such as arts, aeronautics, space exploration, education, computing, robotics, and gaming all stand to benefit equally from BCI technology.

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References

 

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