Brain-Computer Interface: Advancement and Challenges

1 Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh; db.ude.tbub@zorif (M.F.M.); moc.liamg@esc.yojusd (S.C.D.); moc.liamg@ibakmdm (M.M.K.); moc.liamg@uumil.niassoh (A.A.L.)

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Sujoy Chandra Das

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Muhammad Mohsin Kabir

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Aklima Akter Lima

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Md. Rashedul Islam

2 Department of Computer Science and Engineering, University of Asia Pacific, Dhaka 1216, Bangladesh

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Yutaka Watanobe

3 Department of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu 965-8580, Japan; pj.ca.uzia-u@akatuy

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2 Department of Computer Science and Engineering, University of Asia Pacific, Dhaka 1216, Bangladesh

3 Department of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu 965-8580, Japan; pj.ca.uzia-u@akatuy

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Abstract

Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the last decades, several groundbreaking research has been conducted in this domain. Still, no comprehensive review that covers the BCI domain completely has been conducted yet. Hence, a comprehensive overview of the BCI domain is presented in this study. This study covers several applications of BCI and upholds the significance of this domain. Then, each element of BCI systems, including techniques, datasets, feature extraction methods, evaluation measurement matrices, existing BCI algorithms, and classifiers, are explained concisely. In addition, a brief overview of the technologies or hardware, mostly sensors used in BCI, is appended. Finally, the paper investigates several unsolved challenges of the BCI and explains them with possible solutions.

Keywords: brain-computer interface, signal processing, biomedical sensors, systematic review

1. Introduction

The quest for direct communication between a person and a computer has always been an attractive topic for scientists and researchers. The Brain-Computer Interface (BCI) system has directly connected the human brain and the outside environment. The BCI is a real-time brain-machine interface that interacts with external parameters. The BCI system employs the user’s brain activity signals as a medium for communication between the person and the computer, translated into the required output. It enables users to operate external devices that are not controlled by peripheral nerves or muscles via brain activity.

BCI has always been a fascinating domain for researchers. Recently, it has become a charming area of scientific inquiry and has become a possible means of proving a direct connection between the brain and technology. Many research and development projects have implemented this concept, and it has also become one of the fastest expanding fields of scientific inquiry. Many scientists tried and applied various communication methods between humans and computers in different BCI forms. However, it has progressed from a simple concept in the early days of digital technology to extremely complex signal recognition, recording, and analysis techniques today. In 1929, Hans Berger [1] became the first person to record an Electroencephalogram (EEG) [2], which shows the electrical activity of the brain that is measured through the scalp of a human brain. The author tried it on a boy with a brain tumor; since then, EEG signals have been used clinically to identify brain disorders. Vidal [3] made the first effort to communicate between a human and a computer using EEG in 1973, coining the phrase “Brain-Computer Interface”. The author listed all of the components required to construct a functional BCI. He made an experiment room that was separated from the control and computer rooms. In the experiment room, three screens were required; the subject’s EEG was to be sent to an amplifier the size of an entire desk in the control area, including two more screens and a printer.

The concept of combining brains and technology has constantly stimulated people’s interest, and it has become a reality because of recent advancements in neurology and engineering, which have opened the pathway to repairing and possibly enhancing human physical and mental capacities. The sector flourishing the most based on BCI is considered the medical application sector. Cochlear implants [4] for the deaf and deep brain stimulation for Parkinson’s illness are examples of medical uses becoming more prevalent. In addition to these medical applications, security, lie detection, alertness monitoring, telepresence, gaming, education, art, and human enhancement are just a few uses for brain–computer interfaces (BCIs), also known as brain–machine interfaces or BMIs [5]. Every application based on BCI follows different approaches and methods. Each method has its own set of benefits and drawbacks. The degree to which a performance can be enhanced while minute-to-minute and day-to-day volatility are reduced is crucial for the future of BCI technology. Such advancements rely on the capacity to systematically evaluate and contrast different BCI techniques, allowing for the most promising approaches to be discovered. In addition, this versatility around BCI technologies in different sectors and their applications can seem so complex yet so structured. Most of the BCI applications follow a standard structure and system. This basic structure of BCI consists of signal acquisition, pre-processing, feature extraction, classification, and control of the devices. The signal acquisition paves the way to connecting a brain and a computer and to gathering knowledge from signals. The three parts of pre-processing, feature extraction, and classification are responsible for making the associated signal more usable. Lastly, control of the devices points out the primary motivation: to use the signals in an application, prosthetic, etc.

The outstanding compatibility of various methods and procedures in BCI systems demands extensive research. A few research studies on specific features of BCI have also been conducted. Given all of the excellent BCI research, a comprehensive survey is now necessary. Therefore, an extensive survey analysis was attempted and focused on nine review papers featured in this study. Most surveys, however, do not address contemporary trends and application as well as the purpose and limits of BCI methods. Now, an overview and comparisons of the known reviews of the literature on BCI are shown in Table 1 .

Table 1

A summary of recent surveys/reviews on various BCI technologies, signals, algorithms, classifiers, etc.

Ref.	Purposes	Challenges
[6]	Advantages, disadvantages, decoding algorithms, and classification methods of EEG-based BCI paradigm are evaluated.	Training time and fatigue, signal processing, and novel decoders, shared control to supervisory control in closed-loop.
[7]	A comprehensive review on the structure of the brain and on the phases, signal extraction methods, and classifiers of BCI	Human-generated thoughts are non-stationary, and generated signals are nonlinear.
[8]	A systematic review on the challenges in BCI and current studies on BCI games using EEG devices	Biased within the process of search and classification.
[9]	A well-structured review on sensors used on BCI applications that can detect patterns of the brain	The sensors are placed in the human brain when neurosurgery is needed, which is a precarious process.
[10]	A brief review on standard invasive and noninvasive techniques of BCI, and on existing features and classifiers	To build brain signal capture systems with low-density electrodes and higher resolution.
[11]	This paper briefly describes the application of BCI and neurofeedback related to haptic technologies	This study only covers a small domain of BCI (haptic technology)
[12]	This survey mainly focuses on identifying emotion with EEG-based BCI, with a brief discussion on feature extraction, selection, and classifiers	There are no real-life event datasets, and the literature could not sense the mixed feelings simultaneously.
[13]	This paper refers to applying only noninvasive techniques on BCI and profound learning-related BCI studies	This study exclusively covers noninvasive brain signals.
[14]	This review focused on popular techniques such as deep learning models and advances in signal sensing technologies	Popular feature extraction processes, methods, and classifiers are not mentioned or reviewed.

Abiri, R. et al. [6] evaluated the current review on EEG-based various experimental paradigms used by BCI systems. For each experimental paradigm, the researchers experimented with different EEG decoding algorithms and classification methods. The researchers overviewed the paradigms such as Motor imagery paradigms, Body kinematics, Visual P300, Evoked potential, and Error related potential and the hybrid paradigms analyzed with the classification methods and their applications. Researchers have already faced some severe issues while exploring BCI paradigms, including training time and fatigue, signal processing, and novel decoders; shared control to supervisory control in closed-loop; etc. Tiwari, N. et al. [7] provided a complete assessment of the evolution of BCI and a fundamental introduction to brain functioning. An extensive comprehensive revision of the anatomy of the human brain, BCI, and its phases; the methods for extracting signals; and the algorithms for putting the extracted information to use was offered. The authors explained the steps of BCI, which consisted of signal acquisition, feature extraction, and signal classification. As the human brain is complex, human-generated thoughts are non-stationary, and generated signals are nonlinear. Thus, the challenging aspect is to develop a system to find deeper insights from the human brain; then, BCI application will perform better with these deeper insights. Vasiljevic, G.A.M. et al. [8] presented a Systematic Literature Review (SLR) conclusion of BCI games employing consumer-grade gadgets. The authors analyzed the collected data to provide a comprehensive picture of the existing reality and obstacles for HCI of BCI-based games utilizing consumer-grade equipment. According to the observations, numerous games with more straightforward commands were designed for research objectives, and there was a growing amount of more user-friendly BCI games, particularly for recreation. However, this study is limited to the process of search and classification. Martini, M.L. et al. [9] investigated existing BCI sensory modalities to convey perspectives as technology improves. The sensor element of a BCI circuit determines the quality of brain pattern recognition, and numerous sensor modalities are presently used for system applications, which are generally either electrode-based or functional neuroimaging-based. Sensors differed significantly in their inherent spatial and temporal capabilities along with practical considerations such as invasiveness, mobility, and maintenance. Bablani, A. et al. [10] examined brain reactions utilizing invasive and noninvasive acquisition techniques, which included electrocorticography (ECoG), electroencephalography (EEG), magnetoencephalography (MEG), and magnetic resonance imaging (MRI). For operating any application, such responses must be interpreted utilizing machine learning and pattern recognition technologies. A short analysis of the existing feature extraction techniques and classification algorithms applicable to brain data has been presented in this study.

Fleury, M. et al. [11] described various haptic interface paradigms, including SMR, P300, and SSSEP, and approaches for designing relevant haptic systems. The researchers found significant trends in utilizing haptics in BCIs and NF and evaluated various solutions. Haptic interfaces could improve productivity and could improve the relevance of feedback delivered, especially in motor restoration using the SMR paradigm. Torres, E.P. et al. [12] conducted an overview of relevant research literature from 2015 to 2020. It provides trends and a comparison of methods used in new implementations from a BCI perspective. An explanation of datasets, emotion elicitation methods, feature extraction and selection, classification algorithms, and performance evaluation is presented. Zhang, X. et al. [13] discussed the classification of noninvasive brain signals and the fundamentals of deep learning algorithms. This study significantly gives an overview of brain signals and deep learning approaches to enable users to understand BCI research. The prominent deep learning techniques and cutting-edge models for brain signals are presented in this paper, together with specific ideas for selecting the best deep learning models. Gu, X. et al. [14] investigated the most current research on EEG signal detection technologies and computational intelligence methodologies in BCI systems that filled in the loopholes in the five-year systematic review (2015–2019). The authors demonstrated sophisticated signal detecting and augmentation technologies for collecting and cleaning EEG signals. The researchers also exhibited computational intelligence techniques, such as interpretable fuzzy models, transfer learning, deep learning, and combinations for monitoring, maintaining, or tracking human cognitive states and the results of operations in typical applications.

The study necessitated a compendium of scholarly studies covering 1970 to 2021 since we analyze BCI in detail in this literature review. We specialized in the empirical literature on BCI from 2000 to 2021. For historical purposes, such as the invention of BCI systems and their techniques, we selected some publications before 2000. Kitchenham [15,16] established the Systematic Literature Review (SLR) method, which is applied in the research and comprises three phases: organizing, executing, and documenting the review. The SLR methodologies attempted to address all possible questions that could arise as the current research progresses. The recent study’s purpose is to examine the findings of numerous key research areas. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were used to put together the essential materials for this study, which consists of four parts: identification, scanning, eligibility testing, and inclusion. We gathered 577 papers from a variety of sources and weeded out duplicates and similar articles. Finally, we carefully chose 361 articles and sources for monitoring and review. The PRISMA process is presented in Figure 1 .