BRAIN-COMPUTER INTERFACE SYSTEMS REVIEW A HYBRID METHOD FOR INCREASING THE NUMBER OF COMMANDS IN SSV

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BRAIN-COMPUTER INTERFACE SYSTEMS REVIEW A HYBRID METHOD FOR INCREASING THE NUMBER OF COMMANDS IN SSVEPS BCIS Yaqoub E Althuwaini*1, Seyed A Kaboli*2 *1London

South Bank University, School Of Engineering, London, United Kingdom.

*2Cranfield

University, Aerospace Solution Dept., Cranfield, United Kingdom.

ABSTRACT Brain-computer interface (BCI) systems integrated with the steady-state visual evoked potential (SSVEP) provide a broad spectrum of info throughput and need short training than BCI systems utilizing different brain signals. A repetitive visual stimulus (RVS) must be shown to the individual to evoke an SSVEP. Coupling visual templates or additional light triggers designed to produce amplified lighting may be used to make the RVS hold a graphic screen. The SSVEP characteristics are influenced by the RVS attributes (e.g., Frequency, color), dependent on the visualization system. This affects BCI information throughput as well as user protection and convenience. In this paper, the historical evolution of BCIs with particular consideration on SSVEP based BCIs was reviewed, and a hybrid method for increasing the number of commands in SSVEP based BCI system using a combined color and frequency model was proposed for wheelchair control.

Keywords: Brain-Computer Interface (BCI), SteadyState Visual Evoked Potential (SSVEP), Color Detection, Frequency Detection, Psychtoolbox.

I.

INTRODUCTION

Brain-Computer Interface (BCI) constructs a direct correspondence channel between the brain and external devices by coding and decoding mental activities. Recently, Electroencephalogram (EEG) based BCIs have slowly moved from the lab to the public's eyesight, and one can find more application scenarios increasingly, for example, diagnosis, rehabilitation, disability support, and fatigue monitoring. Among various applications, steady-state visual evoked potential (SSVEP) based BCI has attracted a lot more and much more interest because of its increased Information Transfer Rate (ITR), reduced user education, and simple operation.

Scientists have developed many decoding and encoding algorithms to enhance system performance. To facilitate the analysis of algorithms' overall performance, wide-open datasets for SSVEP based BCIs have emerged in the recent past. These wide-open datasets high effectiveness has benefited the reports in highspeed BCI spellers for scientists. Nevertheless, to enhance the practicality of SSVEP based BCIs, a wearable BCI device is in demand that is great. [1] In contrast to the BCI system, which has a regular bulky, expensive, and wired EEG process, a wearable BCI device is much better in practice due to its advantages, for example, portability, easy preparation, mobility, and cost that is low. Nevertheless, in much more complicated locations, wearable BCI methods' practical applications encounter far more difficulties in data acquisition, information analysis, and user experience. As much as we know, a public dataset with many topics for a wearable SSVEP based BCI is missing.

An EEG electrode is a crucial element in using a wearable BCI feature. Wet electrodes and dry electrodes are two kinds of scalp electrodes that are generally utilised to get EEG signals. Wet electrodes have improved signal quality and are far more comfortable to wear. Nevertheless, the wet electrodes' planning before the test requires professional technical assistance and filtering the conductive paste after usage is also time-consuming.

Besides, wet electrodes cannot be used for a very long duration recording when the gel will dry over a length of time. The dry electrode does not need conductive paste and then provides a durable and convenient EEG acquisition method. Besides, the dried-up electrode is ideal for making high-density electrode arrays that collect EEG signals with good spatial resolution. [2] Nevertheless, dried-up electrodes' signal quality and user experience are even worse because of the small media of electrodes onto the head. However, many scientific studies have compared the difference between dry and wet electrodes in EEG recording and BCI applications. A detailed comparison of BCI performance and user experience between the two types of electrodes is missing for a wearable SSVEP based BCI. www.irjmets.com

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For an extended period of method operation, most people will feel fatigued, have drowsiness, trouble focusing, and other dissatisfaction or even uncomfortable emotions to flickering stimulus.

The current study begins with a brief introduction of extensive research and significant events in the BrainComputer Interface evolution path. It should be noted that summarizing all findings within this field is defiantly beyond the scope of a single article, and here, the most important breakthroughs are mentioned, which are biased by the writer's perspective.

II.

METHODOLOY

Historical Evolution of BCI's The historical events are generally considered in three main eras. The first part is shown in Figure 1, corresponding to the initial concept and technology origin.

In the 1920s, a German researcher called Hans Berger was the first to demonstrate that the human mind is making electric currents. These currents represent the mind's dynamic state and could be assessed by placing a series of electrodes on the scalp: the idea of Electroencephalography (EEG) came into this world [3]. EEG proved an essential instrument in neuroscience, mainly to learn cognitive features and their neural correlates for comprehending and diagnosing neuro pathologies. With the improvement of EEG, the concept that brain activity might be used as a correspondence channel or as an information holder is considered broadly.

Kamiya, throughout 1968, has particularly demonstrated that features of EEG pastime - in the studies of his he deemed alpha waves - can intentionally be managed by a human issue after several instructions [3]. This was the beginning of neurofeedback, which teaches people how to self-regulate brain function with accurate data about the workout. This prompted scientists to consider incorporating EEG into their displays. In the early 1970s, Nina Sobell provided people with a virtual representation of a synchronized mental workout to encourage them to synchronize the EEG functions. [4]. Jacques J. Vidal, a Belgian scholar at the University of California, invented the word "Brain-Computer Interface" in 1973. [5] BCIs, according to Vidal, "use real electrical signals in a man-computer interaction" as well as "as a way of controlling external mechanisms such as machines or perhaps prosthetic tools." Just the principles had been formulated at the time, and applications were still in progress - but some of the proposals and the framework outlined are still being researched and implemented today. Although the field remained inactive in the 1970s and early 1980s, many scholars from the United States and Europe pioneered the BCI industry by introducing the first actual and functioning BCI architectures, which established several of the significant paradigms encountered nowadays. www.irjmets.com

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International Research Journal of Modernization in Engineering Technology and Science ( Peer-Reviewed, Open Access, Fully Refereed International Journal ) Volume:03/Issue:11/November-2021 Impact Factor- 6.752 www.irjmets.com Donchin and Farwell presented a novel software in 1988 that introduced the latest and widely used BCI paradigm known as the "P300 speller" [6]. They suggested a BCI for spelled letters based on Event-Related Potentials (ERPs), which are EEG interceptions in response to a particular event or stimulus. A six-by-six checkboard of characters and digits holds a display screen in the P300 speller. This specific grid's columns and rows wink at random, and the individual must count how many times the word he has to spell is blinked.

Whenever the target letter is displayed, an ERP known as the P300 is triggered in the user's EEG indicators, which can be sensed. It becomes simple to detect the column and row containing the letter the individual wishes to spell after a few flashing iterations, thus picking this specific letter. Even though this procedure was only used on stable adults at the time, it showed that BCIs could be used to help chronically paralyzed individuals cope and fulfill their necessities.

Scientists in the United States and Europe introduced BCIs based on Sensory Motor Rhythms (SMR), i.e., oscillatory EEG action, especially the mu rhythm (7 to 13Hz) over the sensory-motor portion of the cortical, soon after. Jonathan Wolpaw and his peers in the United States developed a behavioral conditioningbased BCI for 1D cursor control. Users have been trained to self-regulate the intensity of SMR behavior by voluntarily moving a ball up or down using this technique. Neurofeedback facilitated this, which involves displaying people's SMR behavior in real-time to manipulate it rapidly. At almost the same time, in Europe, Gert Pfurtscheller from Austria worked on another SMR-based BCI, wherein users had to visualize left or right-hand actions, which were then translated into commands by the computer using machine learning. So, the motor visuals BCIs were born from it.

Although still in Europe, Niels Birbaumer has also been busy making progress on the mature product of the BCI paradigm: BCIs dependent on Slow Cortical Potential (SCP). SCPs are low-frequency variations of EEG predictor strength that can be actively lost or gained with neurofeedback and exercise. This basic theory has been used to create the "Thought Translation Device" (TTD), which allows a person to select between one set of commands and another by adjusting the SCP magnitude. People who were paralyzed used the TTD to spell letters. The idea was to use the SCP BCI to choose between two letter types. The chosen letter class was then classified into two categories, and the process continued until only one letter remained in each band, enabling the user to communicate by mental workout alone. Although the SCP BCI is no longer in operation, the TTD stated that BCIs provided services for severely paralyzed users due to relatively inferior displays These forerunners founded the BCI field and are still influential in BCI research today. Their work ignited a dramatic growth in BCI research in the years that followed.

BCI technology became a field of study in and of itself at the end of the twentieth century and the beginning of the twenty-first, with a slew of emerging research institutions entering the fray and propelling the field along at www.irjmets.com

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International Research Journal of Modernization in Engineering Technology and Science ( Peer-Reviewed, Open Access, Fully Refereed International Journal ) Volume:03/Issue:11/November-2021 Impact Factor- 6.752 www.irjmets.com a breakneck pace. New BCI paradigms, such as BCIs based on Steady-State Visual Evoked Potentials, have been presented (SSVEP). SSVEP is oscillatory EEG behavior synchronized to the rate of a flickering visual cue to which the individual is paying attention. The same SSVEP response to any stimulus may be combined with specific BCI commands using multiple stimuli, each with a different flickering frequency. mAlthough machine learning had commonly been used for BCIs, at the time, broader and Biometric machine learning techniques were employed to analyze EEG patterns in a much more efficient way, using supported neural classifiers or vector machines [2]. Before that, the popular Common Spatial Patterns (CSP) spatial filtering technique, which is still used today, was introduced [5].

Common Spatial Patterns (CSP) Ramoser 2000 unctional Near Infrared Spectroscopy (fNIRS) Sitaram 2007 ElectroCorticoGraphy Schalk 2008 First direct brain-to-brain interface.

Duke University 2013 New visual or auditory evoked potentials-based BCIs Gao 2014 Hybrid BCIs Muller 2015 Neural Engineering System Design (NESD) program DAPRA 2016

Figure 3: The Recent Developments In Bcis There are still several ongoing research and studies for different applications of the BCIs. The main applications and implementations of Brain-Computer interfaces are introduced in the proceeding section.

BCI Types and Applications Figure 4 shows a summarised outlook of the different BCI types.

Medical Application The health field contains numerous application forms that could benefit from brain signals related to various therapeutic phases such as prevention, detection, diagnosis, rehabilitation, and restoration. There are factors like smoking, alcoholism, and motion sickness under prevention, while detection and diagnosis comprise health complexities such as tumor, brain disorders, and sleep disorders [9]. Finally, rehabilitation and restoration comprise health issues such as brain stroke, disability, and psychological disorders. However, BCI's application www.irjmets.com

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International Research Journal of Modernization in Engineering Technology and Science ( Peer-Reviewed, Open Access, Fully Refereed International Journal ) Volume:03/Issue:11/November-2021 Impact Factor- 6.752 www.irjmets.com into medical phases depends on the type of signal acquisition utilized, between invasive and non-invasive acquisitions.

Invasive As established earlier, invasive techniques capture the electrophysiological signals through electrodes directly implanted in the brain tissue, particularly the cerebral cortex. The approach can be used in the prevention phase following the influences on Alcohol and smoking's attentiveness on brain waves highlighted by numerous studies. Medical prevention depends on the loss of function and the decreased alertness level associated with tobacco and alcohol abuse[10]. Enhancing the consciousness levels to prevent motion sickness that is influenced by Alcohol and smoking leads to dramatic rates of road accidents related deaths following the decreased levels of alertness studies have proposed various BCI applications. Among these uses a virtual reality-based motion sickness platform designed with a 32 channel EEG system utilized to assess and record motion sickness levels [11]. Nonetheless, for the invasive technique to be used in any medical phase, the subjects must undergo surgery to implant the electrodes. These sensors are often implanted through opening the skull through a surgical procedure known as a craniotomy.

Once the electrodes are placed on the subject's cerebral cortex, they capture electrophysiological signals in an excellent and quality manner. However, invasive acquisition techniques are only used in investigating animal models. Therefore, while they are reported to offer quality signals that would go an extra mile towards enhancing the eminence of the signals in all correlational phases of Healthcare, they have not yet been deployed in these areas.

Non-invasive While researchers have not attempted to use invasive acquisition techniques in various medical phases, noninvasive techniques have been widely used over recent years. As revealed earlier, these approaches are used to catch electrophysiological signals from the scalp instead of using surgery to implant electrodes. This is achieved in the medical field by using electroencephalogram (EEG), functional magnetic resonance imaging, and P-300 based BCI [11]. However, EEG is widely used following its beneficial aspects of ease of use and simplicity. The EEG signals are categorized into two, spontaneous EEGs and Stimulus-evoked EEGs. For instance, non-invasive acquisitions are used in detection and diagnoses where a Tumor generated through uncontrolled self-dividing cells is often discovered through EEG as a cost-effective alternative to CT-SCAN and MRI [9].

For this reason, the EGG-based brain tumors detection mechanism marks as the primary subjects for researchers as they set out to identify breast cancer through EGG signals. In [12], a developed and proposed system identifies EEG abnormalities related to brain tumors and epilepsy. With non-invasive acquisition technologies, such as the EEG, the researchers contend that it could lead to early detection of epilepsy seizures as a common neurologic disorder and efficiently manage or control its effects. In addition to these insights, Dyslexia is another brain disorder that can be diagnosed by assessing brain behavior.

Games and Entertainment Gaming and entertainment applications have expanded into non-medical brain-computer interfaces with BCI's advent. Currently, different games are presented in possibilities where helicopters can fly at any point in 2D and 3D virtual realities. By integrating features of existing games with brain-controlled characters, researchers have focused on this subject to identify and provide a multi-brain entertainment experience for game users [12]. For instance, in the video game known as BrainArena, users can engage in a collaborative football game using two BCIs to score goals by merely imagining left or right movements. More importantly, it has been reported that some of the EEG virtual reality games have been incorporated into neuro-prosthetic rehabilitation. These games comprise modifying the existing one or a new game idea altogether. Gurkok, Nijholt & Poel [13] proposed a Brainball game to reduce users' stress levels. In essence, the players can only move the ball if they are relaxed, where the users who are calmer are more likely to win the game, thereby reducing their stress level while simultaneously being amused.

Remote Control Intelligent environments, including intelligent houses, transportation motors, and workplaces, have also taken advantage of brain-computer interfaces providing luxury, additional security measures, and psychological www.irjmets.com

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International Research Journal of Modernization in Engineering Technology and Science ( Peer-Reviewed, Open Access, Fully Refereed International Journal ) Volume:03/Issue:11/November-2021 Impact Factor- 6.752 www.irjmets.com control to people's daily life [14]. Moreover, intelligent environments are also projected to integrate the Internet of things with BCI, as derived by [15]. In their report, Tello et al. endorsed a cognitive controller system known as Brain-computer interface, based Smart Living Environment Auto-adjustment Control Systems. This intelligent system is an excellent example of the BCI remote control systems as it monitors the user's mental state and adapts to the user's environment accordingly. More importantly, the intelligent system has expanded its functionality by incorporating home networking universal plug-and-play. Also, the environmental contribution of improving brain-computer interface-based home applications in the context of awareness has been included in various research studies.

The Brain signals go an extra mile towards enhancing the workplace conditions by assessing the user's cognitive state. More importantly, the cognitive state monitoring BCI function has profoundly benefited the intelligent transportation field. In essence, drivers' behavior has been a crucial subject among researchers who agree that distraction and fatigue are primary causes of traffic accidents [16]. For this reason, the use of EEG signals for the detection of exhaustion has been widely examined where a multimodal context cognition for the intelligent driving system was presented to predict the fatigue and stress through analyzing EEG signals as well as monitor the speed of the car through concentration value of brain signals. [15] Challenges Ideally, the brain-computer interface's conception and implementation have endured various hurdles. These challenges can be classified under two significant technological aspects, Usability and technical. While usability hurdles revolve around limitations faced due to human acceptance, technical challenges are comprised of system obstacles related to EEG features and characteristic [17]. The limitations on Usability revolve around the acceptance of using BCI technology, such as the training process's hurdle. According to [18], training users is profoundly time-consuming, mainly guiding users through several recorded sessions. This often occurs in the classification and calibration phase, where they are taught how to deal with the system and control their brain signals. Another usability challenge is data transfer rate, a joint assessment metric employed in command brain-computer interface systems. Therefore, this factor presents a problem as it depends on various parameters, impacting the detection's accuracy [11].

Under technical challenges, problems correlated to the brain signals' recorded electrophysiological aspects may arise, such as noise, non-stationary and short training sets. Non-stationary electrophysiological brain signals often show a significant problem in developing the BCI system [19]. It, therefore, commences in a consistent change of user signals over a particular period between the recording sessions. Also, noise is a significant challenge contributing to a non-stationary brain cells issue.

The Future of BCI It would be safe to conclude that BCI's consistent research and development evoke a high degree of excitement among scientists, clinicians, engineers, and the general population. Therefore, this factor shows the bright future for the BCI, particularly with the possibility that they may ultimately be used to replace and restore critical functionality for those living with disabilities through neuromuscular disorders [20]. Moreover, BCI's research and development may also enhance rehabilitation for head trauma individuals, stroke, and other disorders.

Beyond medical applications, numerous complex systems and processes would operate efficiently based on human thoughts in the future. It would be worth noting that BCI's field is still in its early stage and will require more profound insights to capture the quality signals and properly process them. Many researchers have argued that the developments are constrained to recognizing particular expressions, words, and moods [3]. For this reason, future research and development are geared towards recognizing objects as they are seen or interpreted through the human brain. This stride will go the extra mile towards creating additional avenues to understand brain functionality, damage, and repair. More importantly, with enthusiasm among researchers to make significant advancements to the capabilities of the BCIs, it will be possible to identify thoughts in the brain by capturing the right signals from the brain.

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International Research Journal of Modernization in Engineering Technology and Science ( Peer-Reviewed, Open Access, Fully Refereed International Journal ) Volume:03/Issue:11/November-2021 Impact Factor- 6.752 www.irjmets.com Ethical Considerations There is increasing popularity of implantable brain-interfacing devices geared towards treating movement disabilities, mood disorders, behavior, and thoughts. Nonetheless, the use of invasive acquisition techniques to ease the burden of disability is not ethically permitted despite being a profound scientific and clinical human pursuit. As a result, the invasive techniques have been directed towards investigating animal models following The fact that the balance of projected benefits of patients against the anticipated risks instigated by this intervention ought to be thoroughly examined in the context of individual welfare. [21] raise ethical inquiries on identity and personality regarding changes in behavior instigated by the use of BCI that may adversely affect perception, cognition, and well-being. For instance, the researcher derives that exhaustion is common after deep brain stimulation surgery for treating tremor in Parkinson's disease, which further influences the patients' quality of life in question. Understanding the conceptions that patients and caregivers have about personal agency and identity when using BCI systems is crucial for informed consent and for responding to legal issues and public policy regarding human conduct's provenance to devised algorithms [9]. In this regard, it would be imperative to derive that deploying BCI system to enhance the brain or shows a more significant complex scenario not only in regards to the process of weighing whether the benefits of utilizing the system outwit the risks associated but also the impact it may have on the society as a whole.

Evoked potential The electric stimuli calculated through the brain after activation by a few external stimuli are evoked potentials (EP). Evoked potentials are categorized as visual, somatosensory, or auditory based on the stimulus properties.

EP and brain responses triggered by neural mechanisms formed by environmental stimulation or precursory structures for motor activity are all suggested by the event-related prospect (ERP).

Visual evoked potentials Visual evoked potentials (VEPs) are changes in brain function that occur in the sensory cortical in response to a visual stimulus. They are easy to spot because moving the stimuli nearer to the predominant field of vision increases the magnitude of VEPs substantially. VEPs are divided into several categories depending on the relevant criteria:        

Morphology of the optical stimuli VEPs triggered by flashing stimulation VEPs brought on by graphic patterns as checkerboard lattice VEPs by frequency of visual stimulation Transient VEPs (TVEPs): VEPs with evident stimulation frequency less than six Hz Steady-state VEPs (SSVEPs): VEPs with prominent stimulation frequency higher than six Hz. Area stimulation Whole, half, and partial VEPs

The main issue related to these techniques is the user's requirement to gaze at the graphical interface.

Therefore, it would not be applicable for users with innovative level amyotrophic lateral sclerosis (ALS) or individuals with uncontrollable eyes or neck movements.

Steady-state visual evoked Regan dealt with long streams of inputs made up of a monochromatic sinusoidally modulated cortex (25). The "steady-state" visually evoked potentials (SSVEPs) of the imaginative scheme were low frequency-stable VEPs. As a result, steady-state visual evoked potentials (SSVEPs) are identified as the prospect provoked by a frequency over six Hz change inside the visual field.

SSVEP is highly developed in the human brain's cortex areas when an individual is given multiple daily stimuli.

SSVEP is usually obtained from multiple electrode sites around the occipital region, such as Oz, P4, P3, Pz, O2, O1, and others. Though 4-60 Hz is the most commonly used SSVEP frequency band, resonances can also be observed at ten, twenty, forty, and eighty Hz (26).

SSVEP is produced strongly at the human brain's occipital regions when a person is provided with several regular stimuli. SSVEP is generally acquired from several electrode sites as Oz, P4, P3, Pz, O2, O1, and several surrounding locations to the occipital area. While the most often used SSVEP frequency range is 4 -60 Hz, the www.irjmets.com

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The SSVEP is triggered by a visual stimulus given to the eyes for frequencies exceeding 6 Hertz. SSVEP includes a detectable resonance frequency and its harmonic components with the occipital cortex. It is generally accepted that power spectral density (PSD) methods seem to be the most commonly used in SSVEP-based BCIs. for this, A resonance of the PSD is measured within a specified time window and computed from the collected signal. The said amplitude in the visual stimulation yields the peak frequency of data. Spectrogram can be calculated using the Fast Fourier Transform with a meagre computational cost. Whereas traditional spectral methods require an initial cutoff, nonparametric approaches are less complex and easier to implement. Though FFT-based Power Spectral Density has advantages, it has notable drawbacks.

Single bipolar implementation is sensitive to noise and has a lower SNR. Therefore, it is common to use canonical correlation to improve the SNR by providing channel correlated variance info.

A couple of canonical variables would be enhanced to maximize their correlation. A variety of data from multiple electrodes was analyzed to increase the reference signal's reference signal at the stimulation frequency bands and the EEG signals. Therefore, the frequency with maximum correlation was selected as the goal. CCA outperformed PSDA-based BCIs in performance since it has a higher SNR. Therefore, the implementation of SSVE-based BCIs, the CCA, has been the subject of wide deployment.

The simplest and most basic method of detecting stimulus frequency is to use the Fourier transform the size of the EEG signal or its quadratic power (equivalent to the power spectrum density), But this method does not work well and is less used.

The power density analysis method, or PSDA, is one of the oldest processing methods in SSVEP, which in that method the processing takes place in the frequency domain. This method uses Fourier transform and signal power spectrum density, but the main difference is that they use the signal-to-noise ratio of the power spectrum density around the stimulus frequencies instead of the Fourier transform value.

Figure 5: Execution process in CCA for EEG signal Multivariate synchronization index or MSI was offered based on S-estimator theory. S-estimator is a multivariate signal correlation matrix based on entropy values. The S estimator used in the MSI method provides a synchronization index for frequency detection. The frequency detection method with the Lasso Loss function was introduced by Yu Zhang et al. [27]. Lasso is an objective function that identifies linear composition between a set of EEG and reference signals.

The Lasso function is an objective function that will calculate a participation coefficient according to which the linear coefficient conversion function is calculated for each frequency. The most significant participation coefficient represents the stimulus frequency.

In another study, Zanganeh et al.[28] used a combination of 5 frequency detection methods to control a robot.

These five methods included CCA, Lasso, MSI, PSDA, and CCA and PSDA. In the last method, the EEG signal and the reference set go to the frequency domain. To do this, we will use fast Fourier transform or FFT. Then the correlation between the two sets in the frequency domain is calculated. Then, similar to the conventional CCA method, the most significant correlation coefficient equals the stimulus frequency.

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SSVEP in BCIs based wheelchair This study's primary purpose is to increase the number of possible modes in SSVEP-based brain-computer interface systems. As mentioned, with SSVEP, five states could be easily determined in the brain signal. Besides, color recognition is possible in the brain signal, and the primary colors could be recognised easily. Given these facts, it is possible to combine these two properties to design a system that operates, taking advantage of highspeed SSVEP and other benefits of this model. Other innovations could be made in this project. For example, research has shown that geometric shapes are also visible in the SSVEP using the brain signal power spectrum density. To increase the number of modes in the SSVEP method, it is possible to develop a stimuli screen consisting of many options with five colors (white, red, blue, green, and yellow) and four frequencies. In this way, we will have a screen with 20 options, each of which can issue a specific command. The protocol could be implemented with various tools, including Cogent, Psychtoolbox, and BCI2000.

One of the goals pursued in this project is to design a wheelchair. SSVEP-based BCI systems are generally feasible with four frequencies (3 Hz, 5 Hz, 7 Hz, 11 Hz). The four primary directions are determined with these four frequencies: forward, backward, left and right. It is possible to implement such a system with today's methods, but it is challenging to control it. To solve this problem, four sub-directions can be added to the system by combining colors. In this way, the wheelchair can be controlled in 8 directions, as shown in Error!

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Therefore, the user can control the wheelchair by looking in each desired direction. For this purpose, the user needs to gaze at each of the options in Error! Reference source not found.. After the user's attention, it is necessary to extract the recorded signals and process them to identify the users desired direction. It is necessary to identify the user's color and frequency in the processing stage. There are two approaches to this: A. Color and frequency serial detection: In this case, the literature methods can be used in each section.

However, the diagnosis would take more time. The presence of two steps may also increase system error.

Because misdiagnosis of either of the two causes a false detection.

B. Simultaneous color and frequency detection: The color and frequency must be detected simultaneously in this case.

The diagnostic method should be simple step by step in the experimental stage. For this purpose, a graphical interface is designed in which there are only four options with four different frequencies. In this way, the accuracy of the system could be evaluated. Next, a system is designed in which only two colors, red and blue, are flickering at equal frequencies. In this system, a method is designed to help identify the color of options.

Finally, the final system whose user interface is shown in Error! Reference source not found. will be tested.

After designing this graphical interface, the BCI system is combined based on the methods designed in the previous two steps and can determine the user's desired goal. The block diagram of the proposed system is shown in 8:

After determining the frequency and color of the SSVEP stimuli, the target could be turned into a command, and the wheelchair would be commanded to move in the direction desired by the user. www.irjmets.com

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In an SSVEP-based brain-computer interface system, the evaluation criteria are classification accuracy and information transfer rate (ITR).

In one study, Xiaogang Chen et al. [29] used frequency modulation with the help of stimulating color change. In this research, eight options have been designed. Three main frequencies for flashing and two frequencies for modulation have designed. The central frequencies were 10, 12 and 15 Hz, and the modulation frequencies were 1 and 0.5 Hz. Three of the eight designed options flash with the central frequencies and red color. The fourth option flashes at a frequency of 10 Hz and changes its situation from red to green at a frequency of 0.5 Hz. This function creates frequency modulation, and thus the number of commands with three main frequencies can be increased to a more significant number. The fifth and sixth options, respectively, flash at 12 and 15 Hz, change their situation from red to green at 0.5 Hz. The seventh and eighth options respectively flash at frequencies 10 and 12 and change their situation from red to green at 1 Hz. Color efficiency in this project is for frequency modulation, and there is practically no color recognition.

EEG signals collection and processing As illustrated in Figure 8, the first stage of the system configuration involves acquiring the EEG signals and preprocessing the obtained signals. Several methods can be used for preprocessing and analysis of the signals.

Canonical Correlation Analysis or CCA has gained more attention because of its simplicity and higher speed in online applications. However, a method proposed by Zhang et al. l, known as MSI or Multivariate Synchronization Index, also offers advantageous characteristics. In this approach, synchronizing a reference signal and a collected signal generally comprised of noise is evaluated and provides an index for determining the stimulus frequency. Other methods like Power spectral density analysis are also studied; however, these methods might have a reasonable classification accuracy but are sensitive to the high noise and require high computational processing, resulting in a delay in online processing applications.

Stimulus color detection using EEG signal The stimulus color creates different effects on the user's EEG signal. Different methods could be employed for recognizing color based on the stimulation category. For example, to recognize the stimulus color in stimuli based on the P-300 component, the extracted component shape can detect the stimulus color. Common spatial pattern filters, or CSPs, are filters that lead to creating a new time series, that their variance for separation of the two EEG signal categories is maximal. The methods used to design such spatial filters are based on the simultaneous diagonalization of two correlation matrices. This method, called the common spatial pattern, was initially proposed to detect abnormal EEG and classify EEG related to motion imaging. The proposed system flowchart is shown in Error! Reference source not found..

Event-dependent spectral deviation, or ERSP, is a method for studying the dynamics of event-related potentials.

ERSP reflects changes in the density of the signal power spectrum at different frequencies over time. Eventdependent potentials (ERPs) cannot record the entire brain's response to stimuli [30]. Given that ERPs are used as a feature, their efficiency is not good enough. For this reason, Marini has used event-dependent spectral www.irjmets.com

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International Research Journal of Modernization in Engineering Technology and Science ( Peer-Reviewed, Open Access, Fully Refereed International Journal ) Volume:03/Issue:11/November-2021 Impact Factor- 6.752 www.irjmets.com deviation (ERSP), which is a kind of time-frequency conversion, and it is used as a feature in classification, and the results have been significantly improved compared to the extraction of ERPs [31].

To calculate ERSP, the background noise must be removed from the EEG before each stimulus. Then, the stimulus would be displayed in overlapping windows, and the moving average was calculated from the amplitude spectrum. These transformations are normalized to various individual response periods by disporting by their respective background signal. The normalized response transforms the same as the ERSP generated.

III.

CONCLUSION

The integration of the human brain and computer directs that a brain accepts and controls a mechanical device as a regular part of its representation of the whole body. BCI systems tap into various fields, including health care, remote control/ Smart environments and Games, and entertainment. Under Healthcare, BCI systems' application is categorized into two fundamental aspects: invasive and non-invasive. Invasive acquisition techniques raise numerous ethical dilemmas and issues and are not ethically permissible despite being revealed to capture remarkably quality brain signals. On the other hand, non-invasive techniques have been reported to be the most common and widely used approach in bridging through medical possibilities, including prevention, detection, and diagnoses. On the token of Games and entertainment, it has been reported that different games are presented in possibilities where helicopters can fly at any point in 2D and 3D virtual realities. In this sense, researchers have focused on identifying and providing a multi-brain entertainment experience for game users by integrating features of existing games with brain-controlled characters. Ultimately, under remote control, Smart environments, including intelligent houses, transportation motors, and workplaces, have also taken advantage of brain-computer interfaces providing luxury, additional security measures, and psychological control to people's daily life. www.irjmets.com

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International Research Journal of Modernization in Engineering Technology and Science ( Peer-Reviewed, Open Access, Fully Refereed International Journal ) Volume:03/Issue:11/November-2021 Impact Factor- 6.752 www.irjmets.com Despite all these benefits discussed for SSVEP, the system has its drawbacks and weaknesses. One of the most important disadvantages of this system is that only a few modes can be considered. The cause of the problem is frequency constraints. In many usages, the usable frequency band is between 2 and 70 Hz. Therefore, it seems that we can consider a large number of cases.

Nevertheless, in practice, there are many limitations. For example, the function of the human scalp as a lowpass filter causes the elimination of many high frequencies by the scalp. Therefore, in practice, 2 Hz to 20 Hz frequencies and sometimes 30 Hz are employed. On the other hand, power spectrum density estimation methods cannot display high-frequency resonances. Therefore, obtaining a proper resolution in the close frequencies; the system error could increase significantly. Another problem with the system is the presence of harmonics and quasi-harmonics.

IV.

REFERENCES

[1]

F. Zhu, L. Jiang, G. Dong, X. Gao, and Y. Wang, 'An Open Dataset for Wearable SSVEP-Based BrainComputer Interfaces', Sensors, vol. 21, no. 4, p. 1256, Feb. 2021, doi: 10.3390/s21041256.

[2]

F. Lotte et al., 'Introduction : Evolution of Brain-Computer Interfaces To cite this version : HAL Id : hal01656743', 2018.

[3]

N. K. Cauvery, 'BRAIN COMPUTER INTERFACE AND ITS TYPES-AStudy', vol. 8, no. 5, pp. 2015–2018, 1963.

[4]

M. McGregor-Mento, 'The evolution of web art- One pioneer's perspective', IEEE Multimedia, vol. 10, no. 3, pp. 4–8, Jul. 2003, doi: 10.1109/MMUL.2003.1218250.

[5]

J. J. Vidal, 'Toward Direct Brain-Computer Communication, Annual Review of Biophysics and Bioengineering, vol. 2, no. 1, pp. 157–180, 1973, doi: 10.1146/annual.bb.02.060173.001105.

[6]

J. R. Wolpaw, D. J. McFarland, G. W. Neat, and C. A. Forneris, 'An EEG-based brain-computer interface for cursor control', Electroencephalography and Clinical Neurophysiology, vol. 78, no. 3, pp. 252–259, 1991, doi: https://doi.org/10.1016/0013-4694(91)90040-B.

[7]

'Surveying & Measurement'.

[8]

H. Ramos, J. Muller-Gerking, and G. Pfurtscheller, 'Optimal spatial filtering of single-trial EEG during imagined hand movement', IEEE Transactions Rehabilitation Engineering, vol. 8, no. 4, pp. 441–446, Dec. 2000, doi: 10.1109/86.895946.

[9]

L. Carelli et al., 'Brain-Computer Interface for Clinical Purposes: Cognitive Assessment and Rehabilitation, BioMed Research International, vol. 2017, pp. 1–11, 2017, doi: 10.1155/2017/1695290.

[10]

G. Naros and A. Gharabaghi, 'Reinforcement learning of self-regulated β-oscillations for motor restoration in chronic stroke', Frontiers in Human Neuroscience, vol. 9, Jul. 2015, doi: 10.3389/fnhum.2015.00391.

[11]

C. Guger, B. Z. Allison, and G. Edlinger, Eds., Brain-Computer Interface Research. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

[12]

M. Sharanreddy and P. K. Kulkarni, 'An improved approximate entropy-based epilepsy seizure detection using multi-wavelet and artificial neural networks, International Journal of Biomedical Engineering and Technology, vol. 11, no. 1, p. 81, 2013, doi: 10.1504/IJBET.2013.053716.

[13]

H. Gurkok, A. Nijholt, and M. Poel, 'Brain-Computer Interface Games: Towards a Framework BT Handbook of Digital Games and Entertainment Technologies', R. Nakatsu, M. Rauterberg, and P. Ciancarini, Eds. Singapore: Springer Singapore, 2017, pp. 133–150.

[14]

I. Choi, I. Rhiu, Y. Lee, M. H. Yun, and C. S. Nam, 'A systematic review of hybrid brain-computer interfaces: Taxonomy and usability perspectives.', PloS one, vol. 12, no. 4, p. e0176674, 2017, doi: 10.1371/journal.pone.0176674.

[15]

L. Bueno, ‘Interface cérebro-computador baseada em EEG utilizando Redes Neurais auto-organizadas’, Dissertação de Mestrado, p. 145, 2017.

[16]

N. Kosmyna, F. Tarpin-Bernard, N. Bonnefond, and B. Rivet, 'Feasibility of BCI Control in a Realistic Smart Home Environment', Frontiers in Human Neuroscience, vol. 10, Aug. 2016,

www.irjmets.com

@International Research Journal of Modernization in Engineering, Technology and Science

[471]

e-ISSN: 2582-5208

International Research Journal of Modernization in Engineering Technology and Science ( Peer-Reviewed, Open Access, Fully Refereed International Journal ) Volume:03/Issue:11/November-2021 Impact Factor- 6.752 www.irjmets.com doi: 10.3389/fnhum.2016.00416.

[17]

A. Pinegger, H. Hiebel, S. C. Wriessnegger, and G. R. Müller-Putz, 'Composing only by a thought: Novel application of the P300 brain-computer interface', PLOS ONE, vol. 12, no. 9, p. e0181584, Sep. 2017, doi: 10.1371/journal.pone.0181584.

[18]

L. M. McCane et al., 'P300-based brain-computer interface (BCI) event-related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs age-matched controls.', Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, vol. 126, no. 11, pp. 2124–2131, Nov. 2015, doi: 10.1016/j.clinph.2015.01.013.

[19]

E. Y. L. Lew, R. Chavarriaga, S. Silvoni, and J. del R. Millán, 'Single trial prediction of self-paced reaching directions from EEG signals', Frontiers in Neuroscience, vol. 8, Aug. 2014, doi: 10.3389/fnins.2014.00222.

[20]

P. Gargava and K. Asawa, 'Brain Computer Interface for Micro-controller Driven Robot Based on Emotiv Sensors', International Journal of Interactive Multimedia and Artificial Intelligence, vol. 4, no. 5, p. 39, 2017, doi: 10.9781/ijimai.2017.457.

[21]

C. Lai, A. Giuliani, and G. Semeraro, Distributed Systems and Applications of Information Filtering and Retrieval, vol. 515. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014.

[22]

M. Kołodziej, A. Majkowski, and R. J. Rak, 'A New Method of EEG Classification for BCI with Feature Extraction Based on Higher-Order Statistics of Wavelet Components and Selection with Genetic Algorithms BT - Adaptive and Natural Computing Algorithms, 2011, pp. 280–289.

[23]

Yijun Wang, Ruiping Wang, Xiaorong Gao, Bo Hong, and Shanghai Gao, 'A practical VEP-based braincomputer interface', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, no.

2, pp. 234–240, Jun. 2006, doi: 10.1109/TNSRE.2006.875576.

[24]

F.-B. Vialatte, M. Maurice, J. Dauwels, and A. Cichocki, 'Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives, Progress in Neurobiology, vol. 90, no. 4, pp. 418–438, Apr. 2010, doi: 10.1016/j.pneurobio.2009.11.005.

[25]

M. P. Regan and D. Regan, 'Objective Investigation of Visual Function Using a Nondestructive Zoom-FFT Technique for Evoked Potential Analysis', Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques, vol. 16, no. 2, pp. 168–179, 1989, doi: 10.1017/S0317167100028845.

[26]

C. S. Herrmann, 'Human EEG responses to 1? 100�Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena, Experimental Brain Research, vol. 137, no. 3–4, pp. 346–353, Apr. 2001, doi: 10.1007/s002210100682.

[27]

Z. Zhang, X. Li, and Z. Deng, 'A CWT-based SSVEP classification method for brain-computer interface system', in 2010 International Conference on Intelligent Control and Information Processing, Aug. 2010, pp. 43–48, doi: 10.1109/ICICIP.2010.5564336.

[28]

P. Z. Soroush and M. B. Shamsollahi, 'A non-user-based BCI application for robot control', in 2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 - Proceedings, Jan. 2019, pp.

36–41, doi: 10.1109/IECBES.2018.8626701.

[29]

X. Chen, Z. Chen, S. Gao, and X. Gao, 'Brain-computer interface based on intermodulation frequency.', Journal of neural engineering, vol. 10, no. 6, p. 066009, Dec. 2013, doi: 10.1088/1741-2560/10/6/066009.

[30]

S. Makeig, 'Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones', Electroencephalography and Clinical Neurophysiology, vol. 86, no. 4, pp. 283–293, 1993, doi: 10.1016/0013-4694(93)90110-H.

[31]

E. T. Alharbi, S. Rasheed, and S. M. Buhari, 'Feature selection algorithm for evoked EEG signal due to RGB colors', in Proceedings - 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2016, Feb. 2017, pp. 1503–1520, doi: 10.1109/CISP-BMEI.2016.7852955.

www.irjmets.com

@International Research Journal of Modernization in Engineering, Technology and Science

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