Applications and Challenges in Human-Computer
Interaction for EEG-based BCI Systems
MPL Perera
1, SR Liyanage
21, 2Software Engineering, University of Kelaniya, Dalugama, Sri Lanka
Abstract— The Brain Computer Interfacing technology has demonstrated a rapid growth in non-medical applications. The technology has been used extensively in recent times in many fields, such as education, self-government, development, marketing, security, as well as sports and entertainment, mind reading and telecommunication, BCI are extensively used to various therapeutic and rehabilitation purposes as well. Out of the available BCI technologies, which span a few invasive and non-invasive technologies, EEG-based BCI remains the most popular. This article reviews the tools that have been developed for EEG-based BCI and identifies the challenges of current EEG-based BCI which can be considered as part of the broader problems of Human Computer Interfacing. The article also reviews different BCI systems in different modalities that pose significant challenges in usability.
Keywords— Brain-Computer Interfaces, BCI Applications, BCI Challenges
I. INTRODUCTION
The Brain-computer interfacing (BCI) technology is a novel method for user-system communication that can often by-pass the limitations of traditional communication pathways. You would not have to issue commands and complete the exchange [1], [2] with any external devices or muscle involvement. At first, the research community created BCIs for the development of assistive devices with biomedical applications in mind [3]. They also supported the restoration and replacement of missing the engine features for physically challenging or locked-in users [4]. While researchers have encouraged the study of the role of BCIs in the lives of people with paralysis through medical applications, they have also found applications to some extent in computer interface technology in the brain of hyperactive children.
Brain-Computer Interfaces (BCIs) are a kind of user interface that makes it possible, with electrical signals produced in the brain, for users to communicate with computer systems without any movement. This lack of physical interaction with the environment allows physically disabled people the chance to carry on daily behaviour such as walking (for the disordered), internet surfing, playing games and talking, e.g. ALS (Amyotrophic Lateral Sclerosis) or cerebral paralysis [5].
BCI studies have shown strong growth over the past few decades [6].As a result, the technology is closely linked to the promise of making and releasing consumer products with BCI applications. [7].These consumer-grade devices also open up the door for safe consumers to use that same technology with emerging entertainment applications education, Security, games and Communication [8–11].
Nonetheless, because BCI is unpredictable, both hypothetically and, innovation at a beginning phase that requires master information in numerous fields, for example, neuroscience, medication, signal handling, man-made reasoning and HCI, the production of such frameworks, especially with its constraints, present inborn difficulties of the
buyer grade gadgets as of now accessible. Because of games, these issues become clearer as the progression of the game must keep the player pleasant and intriguing. The time goal of the framework or the control message picked can intrude on the progression of the game and make players exhausted, bothered and lose interest in the game. However, nonmedical uses have been expanded to expand the field of study further. More recent trials centred on regular people by investigating the use and development of hand-free applications of BCIs as a new input system. the design of BCI interfaces as discussed in [12], moderate consumers have had some reservations. One of these issues has been the problem of low BCI transmission rate (ITR) and the effect of it on the reduction of user commands.
Fig. 1. Working of Brain-Computer Interfaces.[13]
The brain-computer interface operation is illustrated in Fig. 1, First, BCI devices capture neuronal signals from the brain (step 1), which is called signal acquisition. BCI systems convert these analogue signals into digital signals after signal acquisition (step 2). The features are then extracted and graded utilizing signal processing (step 3 and step 4). The signal
output is then transmitted to BCI applications (step 5). In comparison, some advantages of BCI have been illuminated for capable bodied users [14]. BCI can be valuable where prompt exchanges are troublesome and reaction time for wellbeing applications or applications is significant. They can likewise be utilized to improve the exactness of HCI frameworks that add to BCI in different ventures, for example, business, schooling, promoting, diversion, and shrewd vehicle. The Brain interface must, despite its arranged outcomes, address specialized difficulties and difficulties in comprehending them by clients. Newfound innovation.
The following pages provide a summary of the various methods used to process brain modulation and the electrical changes expressed in brain waves and BCI functions and related applications.
II. BCIDEFINITION,SIGNAL TYPES,CLASSIFICATIONS OF BCISYSTEMS
2.1 Definition of a BCI
A Brain-Computer Interface (BCI) is a correspondence or potentially observing framework which permits the communication between the human psyche and outside gadgets progressively. The BCI interface is likewise at times called a BMI interface. A BCI purchaser intends to change over the BCI framework into an ideal yield, as appeared by cerebrum signals: PC based correspondence or outer gadget control.
Dr J. Vidal introduced the term "brain-computer interface." In the early seventies [15], [16]. BCI Researchers have partaken in this requesting new work considering their desire for giving another creation course to genuinely impaired people and an interest in the more improved human direct guideline of outer frameworks. Scientists originate from various orders, including nervous system science, recuperation, clinical nervous system science and Over the most recent twenty years BCI innovative work has been dangerous, with medication, nervous system science, medication, brain research, software engineering, and arithmetic.[9], [16–18].
BCI is a device that measures, analyzes, and converts brain signals into a real-time output that does not depend on the normal peripheral nerve and muscle output system. [19]. Systems that calculate electrical muscle activity do not follow the definition above and are thus not BCIs. Brain-based systems that calculate the operation of the brain are neither pure nor independent BCIs but can be called based BCIs. A device that uses VEPs for the sensor of the gaze [20], [21] for example, BCI based because it includes neuromuscular guideline of the developments of the eye (or head). It should be noticed that numerous ongoing examinations [22–24] show that specific BCI frameworks dependent on the VEP are not thoroughly look orientated and consequently independent to a little degree.
Nerve cells in the brain are stimulated and produce impulses, which BCI simply transmits electronic and cardiovascular messages and sequences from CNS activity reflections to satisfy the individual's purpose. According to BCI, nerves and muscles and their movements are replaced by
hardware and software that analyze brain signals and convert them into function [10]. Fig. 2 illustrates the senses interact with the motor area of the brain and motor nerves.
Fig. 2. Functional area map of motor cortex for different motor actions of the body [25]
BCI activities depend on the interaction of two adaptive control systems. BCI generates certain brain signals that represent the user's intent, and BCI converts these signals into the user's intention output. BCI should act as an adaptive close loop control device to replace conventional neuronal output channels. This should give the user real-time feedback, which allows the user to modify the brain's also to maximize the desired response. Analytical results are not BCI methods such as real-time interactive recording, analysis, and non-presentation to the user.[26].
2.2 Types of Brain Signals
Signals of varying frequencies emanate from the brain. These signals can be classified as given in Table 1 in separate abilities.
TABLE 1. Types of brain signals
Activity
Band Frequency State of mind
Beta 13-30 Hz active thinking and active attention, Alpha 8-13 Hz Most strongly on the occipital cortex and the
frontal cortex.
Theta 4-7 Hz Emotional tension, intense reflection, and artistic motivation.
Delta 0.5 –4 Hz deep sleep.
Gamma >= 35 Hz Increased mental tasks, including awareness, and understanding
Mu 8-12 Hz Engine activities and motor cortex registered. 2.3 Classifications of BCI Systems
BCI Systems are either dependent or autonomous based on control channels. In addition to brain function, a dependent BCI system often needs a certain level of motor control. However, an autonomous BCI is regulated solely by brain
signals. BCIs are categorized as invasive and non-invasive depending on the positioning of electrodes. An invasive BCI is used for signal acquisition by implanted electrodes. These electrodes may be found in or in the dura itself in the cortical field known as the electrocardiography (ECOG). An over-the-scalp brain activity test is used in a non-invasive BCI device like an electroencephalogram (EEG). BCIs are coordinated and nonconcurrent relying upon their planning. Simultaneous BCIs permit the client to just speak with the objective application for specific timeframes. Nonconcurrent or self-ruling BCI frameworks, notwithstanding, permit client collaborations paying little heed to time [27].
Fig. 3. Classification of brain signal acquisition techniques In recent years, several technologies to measure human brain activity have been developed. Some of the methods calculate the variance of electric activity in the brain's various states, while others calculate different parameters. Due to its invasive, invasive, and toxic nature, the available modalities can be categorized into two groups. Invasive techniques can further be divided into intracortical electrode arrays and electrocorticography (ECOG) technologies. Further orders of non-obtrusive procedures can likewise be delivered in EEG, Near-infrared spectroscopy (NIRS), functional magnetic resonance imaging (fMRI), positron emission tomography (PET) and magnetoencephalography (MEG). Figure 3 displays the primary grouping of brain signals.
2.4 BCI Electrical Signals
Findings considering brain function electrical signals have identified two major approaches [28] for the study of these signals as seen in Fig. 4. The first is to investigate the effects on them, like evocative potential (EP), of different trigger conditions.Its purpose is to detect the oscillation of the brain without being associated with external stimuli, such as Synchronization Desynchronization (ERDnERS) [29].
Fig. 3. BCI electrical signals III. BCIAPPLICATIONS
Literary surveys have clearly shown that the computer interfaces of the brain have also been applied in various fields of study. The author has a special interest in medicine, neuroeconomics and intelligence processing, neuromarketing
and advertising, education and self-regulation, sports and entertainment, security, and verification.
Fig. 4. BCI application fields 3.1 Medical Applications
BCI systems have been mainly developed for medical purposes because they allow patients with any physical disability to move their limbs with an exoskeleton or communicate alone through brainwaves, [10], [30-32]. Their systems have been designated in the first place for medical usage[33].
Abdulkader, Sarah N., Ayman Atia, and Mostafa-Sami M. Mostafa. According to the authors, there are various applications in the healthcare field that can take advantage of brain disorders at all related stages, including prevention, identification, diagnosis, rehabilitation, and rehabilitation.[34]. Decreased ability of a person to maintain self-control can lead to traffic accidents, which can result in death from serious injuries or a variety of illnesses. Motion sickness predictions can contribute to a driver-state monitoring and monitoring system using a set of EEG power indicators. Tested with different brain EEG signals [35], [36], The human auditory level is a BCI-based system that can be measured by the auditory canal as part of the process of collecting sensory information. a virtual reality-based motion-sickness platform with a 32-channel EEG system and joystick used to record motion sickness-level (MSL) in real-time experiments was created. Ness level monitoring has been expanded.[37], [38], [39]. Used to rehabilitate patients with mobility problems. Helps them regain lost function and previous level of mobility or at least adapt to the disabilities they have acquired. Mobility rehabilitation is a physical rehabilitation program.
Besides, BCI programs can be used to predict and diagnose health conditions, such as dysfunctional brain structures (eg, brain cancer), neurological disorders (eg epilepsy), sleep disorders (eg, narcosis), and brain inflammation (eg encephalitis). There are. Both MRI and CT-scans can detect cancer caused by uncontrolled cell division using EEG as a cheap secondary option. EEG was the main research target for brain cancer detection systems [41], [42] and breast cancer diagnoses were investigated using EEG reports.
3.2 Neuro Marketing and Ads
The area of marketing for BCI research was also of interest. The research conducted in [40] clarified the advantages of the EEG assessment for TV ads, both in enterprise and in politics. The BCI-based evaluation tests the attention produced for monitoring activities [41]. The results of another cognitive mechanism in neuromarketing were otherwise considered by [42] researchers. They tried to measure the volume of TV ads by offering another means of measuring the advertisements.
3.3 Security and Authentication
The essential favorable position of biometric verification strategies over different techniques for client confirmation is that they truly do what they should, i.e., they do verify the client. Security systems have biometric-based security connections that are vulnerable to many vulnerabilities, including basic insecure passwords, shoulder navigation, theft, and biometrics. [43] [44], [45]. They may also be of great significance for the physically related handicapped or users [46].
3.4 Games and Entertainment
The demand for non-medical brain-machine interfaces has opened entertainment and gaming software. Several games are featured as [47], where helicopters are rendered in a 2D or 3D virtual world to fly to someplace. Multi-brain technology experimentation is a blend of the characteristics of current games with brain stimulation capabilities[48]. For example, Brain Arena is the name of the video game. The players can participate in using collective or competitive football of two BCIs. of two BCIs. By imagining left or right gestures, you will accomplish goals. Some severe EEG games for emotional control and/or neuroprosthesis have been used, on the other hand. Either a new or a changed game concept is included. The [49] game of brain ball was identified by Tan and Nijholt to decrease the level of stress. Users can only shift the ball by relaxing, so it is more likely that the calmer player will win and therefore learn to control tension while having fun.
From figure 5 illustrate the subjects wear an EEG headset during brain-computer interface games that play with VR games for manipulating virtual objects. In BCI gaming, the subject uses mental commands to allow movement-based action — like "push," "pull" or "jump" — by using a conventional game controller. The BCI processes EEG mental commands and activates the action in the VR game (virtual neural direct interface).
3.5 Educational and Self-Regulation
Although BCI technology is still in its infancy, applications for education are still being explored. Researchers have created a digital learning environment that adapts content based on students' cognitive role measured by an EEG device. Similarly, commercially available education software was released.
This is an active utilization of Neurofeedback. Neurofeedback is a good way to improve brain function by regulating the activity of the human brain. It invades education
systems that use electronic brain combinations to determine the clarity of studies. As a result, individual learning among individual learners develops based on the feedback they receive. Due to this, the individual learning development of the students can be measured rapidly [50]. Also, EEG dependent emotional intelligence has been used to control accompanying tension, as discussed in sports competitions. The integration of different BCI systems to simultaneously measure and stimulate brain activity has recently been successful.
Fig. 5. Brain-computer Interface Gaming
In a hypothetical environment using current technology, students can use their thoughts to enter information into a computer and scan their brains to determine the appropriate zones for administering neurotransmitters that optimize learning. Such speculation only serves to illustrate the various configurations and functions of possible educational BCIs, but in the author's opinion, although technological progress has been made, there are major limitations and challenges. The use of EEG devices for mainstream application is also important; Systems often face trade-offs between processing time, comfort, signal quality and cost. [51] [52]. In [53], BCI technology was developed through Neurofeedback Functional Magnetic Resonance Imaging (fMRI) for self-regulation and training. Recent studies attempt to analyze students' ability to focus from two different backgrounds; An EEG device measures the spontaneous potential of the brain. Due to the availability of low-cost commercial-grade EEG devices, the use of these devices today is not limited to research and clinical purposes, but beyond these applications. This study is an attempt to use the computer's computer interface (BCI) technology to assess cognition. As a result, the performance of the first group was found to be better than that of the second group of students[54].
3.6 Neuroergonomics and Smart Environment
The above facts make it clear that the use of brain signals is not limited to the health sector. Intelligent environments such as smart homes, workplaces or transportation can take advantage of brain-machine interfaces to provide additional security, comfort and physical access to people's daily lives. Cooperation between the Internet of Things (IOT) [55].Brain
signals also help to improve working conditions by measuring the cognitive state of the operator [56]. The effect of mental exhaustion and working time on EEG characteristics are also studied [57]. The office is also a candidate for an intelligent workplace application based on BCI, as provided for in [58]. The device tests a surgeon's stress level and warns the type of response.
Techniques that transform brain activity to generate computer commands in conjunction with the computer's computer interface (BCI) have been used to measure the feasibility of using FUS-based CBI to establish a non - invasive interaction between different species of brains (i.e., human and sprawl mice). The result shows the potential of a computer-mediated BBI linking central neurological functions between two biological entities, presenting unexplored opportunities in the study of neuroscience with potential implications for therapeutic applications[59]. The cognitive state control feature BCI was also used in the field of intelligent transport. In various tests, Driver's conduct was observed. Distraction and fatigue are two major causes of driver inattention and are a powerful cause Most injuries with traffic[60]. Different types of behaviour led to cognitive status determination for the driver [61–63]. Use of EEG signals for the detection of fatigue was widely investigated [64], and [65] explored how drivers' mental health could be assessed by the use of the workload index. A variety of them Models were tested in [66] to differentiate distracted drivers. Lin [67], have indicated that a Brain computer-based Smart Living Environmental Auto-Adjustment Control System (BSLEACS) cognitive controller system should be implemented. It tracks the mental health of the user and adapts the Consequently, the underlying elements. With the involvement of UPnP, it has increased its features in Home networking.
IV. EEGDEVICES
Although the distortion of sound and signal can be caused by EEG signals, they are easy to measure and have a good time resolution. As a result, the EEG is the most widely used method of measuring brain activity in BCI systems. EEG-based instruments directly calculate the electrical potentials of neuronal synaptic processes in the brain. There are several types of neuronal impulses produced by synaptic stimulation of neurons, and their frequency is shown in Table 1. According to the data in the table, gamma waves have the highest frequency (> = 35Hz), and delta waves have the lowest frequency.
TABLE 2. Comparison of BCI devices
Device Sensor Locations Resolution Interface
a) INSIGHT AF3, AF4, T7, T8, Pz 14 bits per channel Wireless b) EPOC+ AF3, AF4, F3, F4,
FC5, FC6, F7, F8, T7, T8, P7, P8, O1, O2
14 bits or 16 bits per channel Wireless c) EPOC FLEX Configurable in any 10-20 location
14 bits per channel slew rate limit 65µV/sample
Wired
d) EPOC X AF3, AF4, F3, F4, FC5, FC6, F7, F8, T7, T8, P7, P8, O1, O2
14 bits or 16 bits per channel
Wireless
Several companies currently manufacture BCI devices, ranging from BCI devices of a clinical standard to BCI devices of a consumer standard. Four common devices are mentioned in Fig. 7 while the key features are shown in Table 2.
Fig. 6. BCI devices.
According to the International 10–20 System[33], This is an internationally accepted method that allows standardizing the positioning of the EEG electrodes. The distance between two electrodes is the same and is proportional to the size and shape of the skull. Covers all areas of the brain (frontal, temporal, parietal, and Occipital).[68]. Fig. 8 illustrates the concept of the 10-20 system.
Fig. 7. The International 10–20 System. From: Nicolas-Alonso and Gomez-Gil (2012)
Fig. 8. International Electrode 10-20 Framework[69]
Emotiv EPOC all-fit headset does not have its terminals put in any of that spot. To make things understood, if you do not mind check Fig. 9 which shows International Electrode 10-20 framework and Fig. 10 which shows Emotiv EPOC's terminal situation [69].
Fig. 9. Emotiv EPOC's terminal situation[69] V. CHALLENGES
The creation of a brain signal communication interface was questioned. It can be graded as technological and usability. The machine obstacles when it comes to EEG features are technological challenges. Challenges in usability are limits Impacting the human acceptability standard[29]. Mainly divided into usability challenges and Technical challenges. Under usability challenge training process and information transfer rate. There are four main Technical challenges, such as Non-linearity, Nonstationary and noise, small training data set and high dimensionality curse.
Fig. 10. Challengers 5.1 Usability Challenges
a) Training process
Training is an activity that takes time to guide the user through the process or the number of sessions recorded. This is achieved during the initial or calibration stages of the classification [70]. The Utility is taught both to manage the device and to monitor its brain input signals in the preliminary process, while the qualified subjects are used to learn the classifier used throughout the calibration phase
b) Information transfer rate
It is the most used BCI assessment metric. It depends on the number of options, the precision of the goal detection and
the average selection time. Thus, selective focus strategies hit higher ITR than BCI imaging because their options are bigger [49].
5.2 Technical Challengers a) Non-linearity
The cerebrum is an exceptionally unpredictable non-direct framework where neural segments can recognize clamorous conduct. EEG signals are consequently best separated from straight methodologies by unique nonlinear methodologies. b) Nonstationarity and noise
A major problem in designing a BCI device is the non-static feature of electrocardiogram brain signals. Mental and emotional conditions may lead to the variability of EEG signals across various sessions. Noise is also a significant contributor to BCI technology issues and the problem of non-stabilization. It produces undesired signals due to changes in the location of electrodes and ambient noise[4], [71].
c) Small training sets
While comprehensive training courses take the subjects time and demand, they provide the individual with the requisite expertise in coping with the system and in managing his neurological signals. Thus, an important challenge in the development of a BCI is a balance between the technical complexity of brain signal interpretation and the level of training necessary for effective interpretation Gui working[72].
VI. CONCLUSION
This article presents a systematic literature review of BCI definition, code types, BCI system classification, and applications. This article focuses on EEG devices and their application support. The brain signals represent the activities treated and regulating actions of the brain or the effect of the information obtained either from sensing or inner organs from other body parts. Interface Brain Interface creates a conduit between the brain and external computers.
The research group has been attracted by BCI applications. Several study areas such as the medical, education, Neuromarketing and ads, game and entertainment fields, and safety and authentication fields were provided for the increasing interest in the BCI application fields. The instruments used to detect brain impulses are also illustrated. As further study author will focus on how BCI connection with the military.
Author Contributions
MPL Perera conceived the idea, collected, and wrote the paper.
Declaration of Conflicting Interests
This author declares no potential conflicts of interest to this research, authorship, and/or publication of this paper.
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