St. Xavier's College (Autonomous),
Mumbai, Maharashtra, India
Human beings have been continuously developing the way in which we interact with computers. They initially used a mouse and then shifted to fingers. But what if we could control it through our brains? This is very much possible with the help of the Brain-Computer interface. Brain-computer interface (BCI) or brain-machine interface (BMI) as a hardware and software communications strategy that empowers humans to interact with their surroundings with no inclusion of peripheral nerves (PNS) or muscles by utilizing control signals produced from electroencephalographic activity this was defined by (Nicolas-Alonso & Gomez-Pilar, 2015). BCI behaves like a direct bridge connecting wired or enhanced brains and an external device. It can be used anywhere and everywhere.
Hans Berger recorded electrical activity in the human brain in 1924 using electroencephalography (EEG) which helped in detecting brain disease. In 1969, studies of Fetz and colleagues were able to observe the monkey operating a meter arm with neural activity. The term “BCI” was coined in 1970 by Professor Jacques Vidal and the first peer review article was put out by him. Later in 1988, Stevo Bozinovski,et. al. were successful in using EEG to control robots. Later in 1998, the first BCI was implanted into a human being by researcher Philip Kennedy. In 2001, the first commercial BCI product was launched in the market. Around 2010 it was proposed that BCI could be employed to restore body function by neural stimulation.
All the BCI systems setup has three components: electrodes, processing pipeline and computer or external device and have five essential components: brain activity, pre-processing, feature extraction, classification and translation into a command. (Marshall, 2013). Whenever we think of something the neurons get activated in a certain part, they generate signals. For this signal to be read by the device requires help from the algorithm. The raw EEG data which are signals have to be pre-processed as there is contamination in EEG data known as “artefacts” and this is eliminated by using spectral and spatial filters to maximize signal to noise ratio. Then significant information from these signals is described using values called “features” and they are investigated. These selected features then go through classification steps where they go via various deep learning and machine learning algorithms. Ultimately, the classified results are converted into device commands for BCI devices.
BCI systems can be branched according to factors like reliability, synchronization and recording type. ( Figure 1)
Figure 1: Classification of BCI
The recording method is the most used. Which has two categorised as:
INVASIVE: The neural activity is measured by implanting microelectrodes, arrays of electrodes or strips of electrodes on the cortex surface. They gave more accurate reads but extra care is needed as scar tissues can form. There are two subtypes:
Intracortical Recording: The action signal of individual neurons is evaluated by embedding a single electrode or array of electrons under the cortex surface of the brain.
ECoG (Electrocorticography): Electrode strip or grid is imbedded on the cortex surface
NON-INVASIVE: The neural activity is measured without any permanent implantation to the subject's brain. There is no surgical process and can be removed whenever the subject feels like it. There are five types:
Functional magnetic resonance imaging (fMRI): Neural activities in the brain are correlated with the variations of the bloodstream.
Functional near-infrared spectroscopy (fNIRS): the neural activity is sensed with the assistance of blood flow using the near-infrared light range.
Magnetoencephalography (MEG): The electrical current taking place naturally in the brain, this generates a quantifiable magnetic field.
Positron Emission tomography (PET): Use energy from radioactive isotopes and energy emitting atomic nucleus to notify any abnormality.
Electroencephalogram (EEG): The neurotransmission and electrical activity is assessed by means of electrodes placed strategically on the scalp. This method is used mostly for commercial purposes. It can be further divided into spontaneous systems which as motor, non-motor and Slow Cortical Potential (SCP) and non-spontaneous has Error-related potential (ErrP), Steady-state Evoked Potential (SSEP) and P300.
BCI has applications in almost all fields of life. BCI can be used to identify and predict brain disorders, narcolepsy (sleep disorder) and brain tumours, it helps in rehabilitation and restoration of muscle or body after accidents, stroke or motor neuron disease like amyotrophic lateral sclerosis (ALS) by using devices like a wheelchair, robotic limbs implantation or exoskeleton. BCI can be used to create smart environments like smart offices, houses and transportation where this system monitors health both normal and mental, attentiveness, improved interaction, safety, person adapting capacity to changes and many more small things which have an effect on the body. Using BCI devices to decide clarity of studied information and monitoring other factors like focus, strengths, stress level, etc. It also has opened a new path of research and market for hyper-realistic games which combine the features of existing games with brain controlling capabilities. Another field that is new is neuromarketing and neuro-advertisement where subject reactions are recorded and decoded. All types of security systems are vulnerable, therefore, it is encouraged to explore bio-signals as they cannot be seen by external observers which would have many added advantages like getting notifications and warnings if anyone tries to access the information.
To get a better insight into what people think about BCI devices a survey was conducted. The questions in the survey were the most situational type of questions based on most of the applications mentioned above. It was observed that people would surely like to use these devices as they make life easier, help in making decisions and work, and monitoring things would be easy. But still many of them wouldn’t prefer using it for various reasons like implantation needed, would prefer the way things are, technology is developing and life will be too much of automation won’t be beneficial. Many individuals have differences of opinion depending on the situation they are put in. People also had many suggestions like using quantum encryption with entanglement, devices that are more sensitive to neuronal activity, having features that can be personalized and predicting the outcomes are some of the interesting ones. It can be said that BCI can work as both, a boon or a bane depending on how it is used.
Figure 2: Graph-based on the survey
As BCI devices are still in the initiation phase of development, many of the long term problems are still not identified. There are also concerns about the security of data. have security patches. Then there are ethical concerns and the cost of BCI devices is high. Brain neuron signals can be of two types: electrical and chemical, BCI devices are unable to pick chemical signals. In some cases, people become dependable on them. Implementation of invasive detectors can leave scar tissues and have problems in recovery.
Interaction between the brain and machines may sound like a science fiction movie but it is possible due to the impressive advancement in the field. People from different backgrounds are interested in this technology and many studies are carried out. People are trying to implement it in every possible way. It is a technology with the potential to change the world.
Abdulkader, S. N., Atia, A., & Mostafa, M.-S. M. (2015). Brain-computer interfacing: Applications and challenges. Egyptian Informatics Journal, 16(2), 213–230. https://doi.org/10.1016/j.eij.2015.06.002
Fabien Lotte, Chang S. Nam, Anton Nijholt. Introduction: Evolution of Brain-Computer Interfaces. Chang S. Nam; Anton Nijholt; Fabien Lotte. Brain-Computer Interfaces Handbook: Technological and Theoretical Advance, Taylor & Francis (CRC Press), pp.1-11, 2018, 9781498773430. ffhal-01656743
Marshall, D., Coyle, D., Wilson, S., & Callaghan, M. (2013). Games, gameplay, and BCI: The state of the art. IEEE Transactions on Computational Intelligence and AI in Games, 5(2), 82–99. https://doi.org/10.1109/tciaig.2013.2263555
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Nicolas-Alonso, L. F., Corralejo, R., Gomez-Pilar, J., Álvarez, D., & Hornero, R. (2015). Adaptive semi-supervised classification to reduce intersession non-stationarity in multiclass motor imagery-based brain–computer interfaces. Neurocomputing, 159, 186–196. https://doi.org/10.1016/j.neucom.2015.02.005
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