How Does Brain-Computer Interface Work

Brain-computer interfaces are the source of links between the brain and external devices like computers. They can substitute or strengthen the peripheral working capacity of human beings involving no muscular movements and provide a great degree of freedom in multiple fields, including gaming, neuroscience, rehabilitation, robotics, and affective computing.

BCI doesn’t require neurotransmitters, chemical substances produced at the junction of neurons, called synapses, used to transmit information and commands from the Central Nervous System to the Peripheral Nervous System. For example, if you want to move your hand, the brain will signal the motor neurons, innervating the target muscles through neurotransmitters. However, BCI is free from any sort of chemical pathway. It requires signals that are analyzed, interpreted, and translated to commands relayed on output devices to carry out multiple desired functions[1].

If we dig into the history, Brain-Computer Interface was envisioned as the potential substitute for neural rehabilitation and serving assistive devices directly controlled and directed by the brain. In 1973 J.J. Vidal made the first attempt to practically implement BCI technology by using Electroencephalogram to record the electrical activity and signals of the cerebral cortex from an intact skull[2].

How Does BCI Work

BCI works by re-exciting or artificially augmenting neural plasticity in malfunctioned neural circuits. BCI exploits undamaged emotional and cognitive functions. It re-establishes the link between peripheral sites and the brain[3].

To build a Brain-Computer Interface, five to six components are required: signal acquisition during experimental paradigm, pre-processing, features extraction (i.e., SSVEP, P300 amplitude, and alpha/beta bands), detection (classification), translation of classifications for commands (BCI application), and user feedback. Many software has been developed for quick analyses and processing, like EEGLab, BCI2000, FieldTrip, and Brainstorm. Such software is based on AI programs and advanced image and signal processing for source/sensor level analysis[4][5]. A brief detail of components is as below:

Signal Acquisition:

The first and essential component of the BCI system is the signal acquisition by measuring the brain signals using sensory modalities (scalp, fMRI for metabolic activity, and intracranial electrodes to measure physiological activity). After the acquisition, signals are amplified to the level at which these can be processed further. Apart from amplification, unnecessary sounds are removed from these signals (i.e., noise is filtered). After filtering, these signals are transmitted to the computer for digitalization.

Feature Extraction:

Pertinent signal characteristics (desired signals) are extracted in this procedure to distinguish them from undesired signals through computer analysis. Then these are transformed into a compact form which is feasible to be translated into output commands. Features must be correlated with the user’s intent. Commonly extracted signal characteristics are firing rates of cortical neurons, time-triggered ECoG response latencies and amplitude, and power within ECoG or EEG. Physiological artifacts like electromyographic signals and environmental artifacts like noise are filtered and avoided to get accurate signals.

Feature Translation:

Extracted features are subjected to feature translation algorithms that convert these features into commands for output devices, i.e., power decrease in a frequency band means the upward displacement of the cursor on the computer. Similarly, p300 potential is translated for selecting the letter which evoked it. The translation algorithm should be dynamic to adapt learned or spontaneous changes in signal features to ensure that a possible range of features covers the whole of device controls.

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Device Output:

Commands which are translated from extracted features control and operate the external device. It is carried out through cursor movement, letter selection, robotic arm operation, etc. Hence, device output controls the user’s feedback, and hence this BCI loop is completed.

Types of BCIs

There are two types of BCIs based on electrodes usage, invasive and non-invasive.

  • In invasive BCI, electrodes are applied on the scalp of the human, i.e., EEG-based BCI. While in invasive BCI, the electrodes are directly attached to the brain carrying out surgeries. i.e., electrocorticography (ECoG) and intracranial electroencephalography (iEEG)[6].
  • Non-invasive methods are safer than invasive ones because invasive methods require surgeries that may alter the psychology of the host and make one prone to infectious diseases.

BCI Applications in Different Fields

Brain-Computer Interface and Aging:

Aging is an inevitable process everyone experiences. It certainly decreases brain and body functions. Even the healthier old age people experience a decline in brain function and memory[7]. Moreover, the brain’s parenchyma shrinks, and blood flow towards the brain decreases the neurotransmitter function. All these physiological changes might lead them to anxiety or depression, which may increase their dependence on others. That is where BCI comes into play, making the lives of aged and disabled people easier by lessening their dependence on others. BCI can be adaptive, assistive, and rehabilitative technology that reads or interprets older adults’ brain activity and transforms into signals that can be used to operate the 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 a command for external devices by directly sending them neural response[8].

BCI can be useful for aged and disabled people equally in enhancing the quality of life by assisting them in daily chores, communicating and building a relationship with their family, and being self-sufficient. BCI can be used effectively to restore learning and improve attention, memory, and consciousness in elderly patients suffering from cognitive impairment[9].

Brain-Computer Interface and Medicine:

Despite intact cognition, many neurological disorders impair voluntary movements, motor functions, and communication. In the last two decades, multiple BCI approaches have been developed based upon slow waves, EPs (Evoked Potentials), SSEPs (steady-state evoked potentials), and MI paradigms (Motor imagery) to help escalate the medical field through BCI. The first BCI application was used to move prosthetics, spell, and move the computer cursor.

The role of BCI is widespread in medicine, and it is used in automated wheelchair control through the BCI approach; another exclusive clinical application of BCI is the facilitation of motor function after spinal cord surgery or stroke. BCIs for rehabilitation are being implied along with conventional assistive devices like transcranial direct current stimulation (tDCS) and functional electrical stimulation (FES- based) neuro-prostheses to increase 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 dysfunctionfor improving emotional and social behavior in autism spectrum disorder[10] and rehabilitation of cognitive disorders related to dementia. Another exciting application of BCI is detecting inner speech without any external speech stimulations[11].

BCIs can be helpful in the diagnosis of certain brain disorders. It can be used to detect neural signals of the cognitive process in patients diagnosed with brain disorders of consciousness (DOC)[12]. It plays a role in improving visual assessment in glaucoma, helping give real-time brain mapping for multiple neurosurgeries[13], detecting intra-operative assessment during anesthesia, and screening cognitive functions in cases of complete immobility[14]. For restoration of mobility in motor impairment patients, the invasive method of intra-cortical recording is better than non-invasive methods[15].

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

Advanced computers in the future will be expected to have perceptual and emotional capabilities, which will assist humans and help in decision making[16]. Recent research suggests that BCI is a potent tool for investigating different affective states and expanding clinical application into psychology. EEG-based BCI can be used to detect and interpret negative and positive emotions induced by video stimuli.[17]

Artistic BCI refers to the integration of arts into BCI. David Rosen boom was the first to try different experiments to link brain functions with the musical production, perception of proprioception, and musical forms[18]. Other instances of Artistic BCI include video gaming, affective state detection, controlling virtual/augmented reality environments. Users can fully control, operate and enjoy video games through SSVEP-BCI. Another exciting feature is that multiple users can participate in a game that requires joint decision-making to control the game[19]. Apart from that, modification in the BCI setup can help investigate how people interact in diversified social contexts, extending the applications in sociology[20].

Virtual/ Augmented Reality (V/AR) technologies and BCI have multiple arts and neurofeedback implications. Brain painting allows you to draw lines on canvas without moving your hands, which is quite an easy way of communication for people suffering from paretic motor impairment[21].

BCI-driven robotic controllers also offer advanced assistive technology to people with mobility constraints. EEG-based mobile robots or wheelchair has demonstrated thesuccess of this technology. Apart from that, robots are being used at dangerous places, i.e., BCI-controlled robots used in coal mining. BCI robots are also being used to increase the astronauts’ working capacity, safety, efficiency, and functionality[22].

Challenges and Concerns related to BCI

Undoubtedly, the inventions and breakthroughs of the Brain-Computer Interface in multiple fields of medicine, engineering, the computer, and space are astonishing. But there are some social and ethical concerns regarding the application of BCI. Many factors like user safety, privacy protection, ethics, community acceptance, data confidentiality, and socioeconomic aspects related to BCI ensure user satisfaction[23]. Users’ physical and mental safety is compromised in some instances; invasive procedures like intracranial microelectrodes and deep brain stimulations may cause post-operative neurological and psychological effects. Implanted microelectrodes can alter emotions, thoughts, feelings, personality, and memories. So, strict guidelines and care are required to ensure safety and ethical measures[24].


We can conclude that the future belongs to the Brain-computer interface if it is executed efficiently. Brain-computer interface is an advanced technology that would uplift technical, medicine, and multiple other fields if used with positive and constructive thinking. They have uncountable benefits, including lessening the lag between moving the mouse and cursor, which is quite handy in the military and gaming. BCIs clinical applications in medicine are tremendous, making the lives of paralyzed ones easier and lessening dependence. Arts, aeronautics, space, education, computer, robotics, and gaming all are equally benefited from BCI.

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  1. Shih JJ, Krusienski DJ, Wolpaw JR. Brain-Computer Interfaces in Medicine. Mayo Clin Proc. 2012;87(3):268. doi:10.1016/J.MAYOCP.2011.12.008
  2. Vidal JJ. Toward direct brain-computer communication. Annu Rev Biophys Bioeng. 1973;2:157-180. doi:10.1146/ANNUREV.BB.02.060173.001105
  3. Vansteensel MJ, Pels EGM, Bleichner MG, et al. Fully Implanted Brain-Computer Interface in a Locked-In Patient with ALS. N Engl J Med. 2016;375(21):2060-2066. doi:10.1056/NEJMOA1608085
  4. Online classification algorithm for eye-movement-based communication systems using two temporal EEG sensors – ScienceDirect. Accessed December 29, 2021.
  5. Dong E, Li C, Li L, Du S, Belkacem AN, Chen C. Classification of multi-class motor imagery with a novel hierarchical SVM algorithm for brain-computer interfaces. Med Biol Eng Comput. 2017;55(10):1809-1818. doi:10.1007/S11517-017-1611-4
  6. Daly JJ, Huggins JE. Brain-Computer Interface: Current and Emerging Rehabilitation Applications. Arch Phys Med Rehabil. 2015;96(3 0):S1. doi:10.1016/J.APMR.2015.01.007
  7. Murman DL. The Impact of Age on Cognition. Semin Hear. 2015;36(3):111. doi:10.1055/S-0035-1555115
  8. Shao L, Zhang L, Belkacem AN, et al. EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface. J Healthc Eng. 2020;2020. doi:10.1155/2020/6968713
  9. Buch VP, Richardson AG, Brandon C, et al. Network Brain-Computer Interface (nBCI): An Alternative Approach for Cognitive Prosthetics. Front Neurosci. 2018;12(NOV). doi:10.3389/FNINS.2018.00790
  10. Amaral C, Mouga S, Simões M, et al. A feasibility clinical trial to improve social attention in Autistic Spectrum Disorder (ASD) using a brain-computer interface. Front Neurosci. 2018;12(JUL):477. doi:10.3389/FNINS.2018.00477/BIBTEX
  11. Martin S, Iturrate I, Millán J del R, Knight RT, Pasley BN. Decoding inner speech using electrocorticography: Progress and challenges toward a speech prosthesis. Front Neurosci. 2018;12(JUN):422. doi:10.3389/FNINS.2018.00422/BIBTEX
  12. Annen J, Blandiaux S, Lejeune N, et al. BCI performance and brain metabolism profile in severely brain-injured patients without response to command at bedside. Front Neurosci. 2018;12(JUN):370. doi:10.3389/FNINS.2018.00370/BIBTEX
  13. Jiang T, Liu S, Pellizzer G, et al. Characterization of Hand Clenching in Human Sensorimotor Cortex Using High-, and Ultra-High Frequency Band Modulations of Electrocorticogram. Front Neurosci. 2018;12:110. doi:10.3389/FNINS.2018.00110/BIBTEX
  14. Lulé D, Hörner K, Vazquez C, et al. Screening for cognitive function in complete immobility using brain-machine interfaces: A proof of principle study. Front Neurosci. 2018;12(AUG):517. doi:10.3389/FNINS.2018.00517/BIBTEX
  15. Hochberg LR. Intracortical Brain-Computer Interfaces for the Restoration of Communication and Mobility. Biophys. 2013;104:376a. doi:10.1016/j.bpj.2012.11.2094
  16. Huang H, Xie Q, Pan J, et al. An EEG-Based Brain-Computer Interface for Emotion Recognition and Its Application in Patients with Disorder of Consciousness. IEEE Trans Affect Comput. 2019;12(4):832-842. doi:10.1109/TAFFC.2019.2901456
  17. Piho L, Tjahjadi T. A Mutual Information Based Adaptive Windowing of Informative EEG for Emotion Recognition. IEEE Trans Affect Comput. 2020;11(4):722-735. doi:10.1109/TAFFC.2018.2840973
  18. Rosenboom D. Active imaginative listening-a neuromusical critique. Front Neurosci. 2014;8(8 JUL). doi:10.3389/FNINS.2014.00251
  19. Sekhavat YA. Battle of minds: a new interaction approach in BCI games through competitive reinforcement. Multimed Tools Appl 2019 795. 2019;79(5):3449-3464. doi:10.1007/S11042-019-07963-W
  20. Amaral CP, Simões MA, Mouga S, Andrade J, Castelo-Branco M. A novel Brain-Computer Interface for classification of social joint attention in autism and comparison of 3 experimental setups: A feasibility study. J Neurosci Methods. 2017;290:105-115. doi:10.1016/J.JNEUMETH.2017.07.029
  21. Botrel L, Holz EM, Kübler A. Brain Painting V2: evaluation of P300-based brain-computer interface for creative expression by an end-user following the user-centered design. 2015;2(2-3):135-149. doi:10.1080/2326263X.2015.1100038
  22. De Negueruela C, Broschart M, Menon C, Del R. Millán J. Brain-computer interfaces for space applications. Pers Ubiquitous Comput. 2011;15(5):527-537. doi:10.1007/S00779-010-0322-8/FIGURES/1
  23. Jebari K. Brain-Machine Interface and Human Enhancement-An Ethical Review. doi:10.1007/s12152-012-9176-2
  24. Goering S, Yuste R. On the Necessity of Ethical Guidelines for Novel Neurotechnologies. Cell. 2016;167(4):882-885. doi:10.1016/J.CELL.2016.10.029

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