Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.

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Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

The brain–computer interface (BCI) is an emerging technology that has the potential to revolutionize the world, with numerous applications ranging from healthcare to human augmentation. Electroencephalogram (EEG) motor imagery (MI) is among the most common BCI paradigms that have been used extensively in smart healthcare applications such as post-stroke rehabilitation and mobile assistive robots. In recent years, the contribution of deep learning (DL) has had a phenomenal impact on MI-EEG-based BCI. In this work, we systematically review the DL-based research for MI-EEG classification from the past ten years. This article first explains the procedure for selecting the studies and then gives an overview of BCI, EEG, and MI systems. The DL-based techniques applied in MI classification are then analyzed and discussed from four main perspectives: preprocessing, input formulation, deep learning architecture, and performance evaluation. In the discussion section, three major questions about DL-based MI classification are addressed: (1) Is preprocessing required for DL-based techniques? (2) What input formulations are best for DL-based techniques? (3) What are the current trends in DL-based techniques? Moreover, this work summarizes MI-EEG-based applications, extensively explores public MI-EEG datasets, and gives an overall visualization of the performance attained for each dataset based on the reviewed articles. Finally, current challenges and future directions are discussed.

Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review

Over the last few decades, the Brain-Computer Interfaces have been gradually making their way to the epicenter of scientific interest. Many scientists from all around the world have contributed to the state of the art in this scientific domain by developing numerous tools and methods for brain signal acquisition and processing. Such a spectacular progress would not be achievable without accompanying technological development to equip the researchers with the proper devices providing what is absolutely necessary for any kind of discovery as the core of every analysis: the data reflecting the brain activity. The common effort has resulted in pushing the whole domain to the point where the communication between a human being and the external world through BCI interfaces is no longer science fiction but nowadays reality. In this work we present the most relevant aspects of the BCIs and all the milestones that have been made over nearly 50-year history of this research domain. We mention people who were pioneers in this area as well as we highlight all the technological and methodological advances that have transformed something available and understandable by a very few into something that has a potential to be a breathtaking change for so many. Aiming to fully understand how the human brain works is a very ambitious goal and it will surely take time to succeed. However, even that fraction of what has already been determined is sufficient e.g., to allow impaired people to regain control on their lives and significantly improve its quality. The more is discovered in this domain, the more benefit for all of us this can potentially bring.

Summary of over Fifty Years with Brain-Computer Interfaces-A Review.

Review of Machine Learning Techniques for EEG Based Brain Computer Interface

Recent advances in neuroscience have paved the way to innovative applications that cognitively augment and enhance humans in a variety of contexts. This paper aims at providing a snapshot of the current state of the art and a motivated forecast of the most likely developments in the next two decades. Firstly, we survey the main neuroscience technologies for both observing and influencing brain activity, which are necessary ingredients for human cognitive augmentation. We also compare and contrast such technologies, as their individual characteristics (e.g., spatio-temporal resolution, invasiveness, portability, energy requirements, and cost) influence their current and future role in human cognitive augmentation. Secondly, we chart the state of the art on neurotechnologies for human cognitive augmentation, keeping an eye both on the applications that already exist and those that are emerging or are likely to emerge in the next two decades. Particularly, we consider applications in the areas of communication, cognitive enhancement, memory, attention monitoring/enhancement, situation awareness and complex problem solving, and we look at what fraction of the population might benefit from such technologies and at the demands they impose in terms of user training. Thirdly, we briefly review the ethical issues associated with current neuroscience technologies. These are important because they may differentially influence both present and future research on (and adoption of) neurotechnologies for human cognitive augmentation: an inferior technology with no significant ethical issues may thrive while a superior technology causing widespread ethical concerns may end up being outlawed. Finally, based on the lessons learned in our analysis, using past trends and considering other related forecasts, we attempt to forecast the most likely future developments of neuroscience technology for human cognitive augmentation and provide informed recommendations for promising future research and exploitation avenues.

/pdf/neurotechnologies-for-human-cognitive-augmentation-current-26n1wt4q3v.pdf

Neurotechnologies for Human Cognitive Augmentation: Current State of the Art and Future Prospects

A Brain–Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field. Although many BCI-spellers have been developed during the last few decades, to our knowledge, no reviews have described the different spellers proposed and studied in this vital field. The presented speller systems are categorized according to major BCI paradigms: P300, steady-state visual evoked potential (SSVEP), and motor imagery (MI). Different BCI paradigms require specific electroencephalogram (EEG) signal features and lead to the development of appropriate Graphical User Interfaces (GUIs). The purpose of this review is to consolidate the most successful BCI-spellers published since 2010, while mentioning some other older systems which were built explicitly for spelling purposes. We aim to assist researchers and concerned individuals in the field by illustrating the highlights of different spellers and presenting them in one review. It is almost impossible to carry out an objective comparison between different spellers, as each has its variables, parameters, and conditions. However, the gathered information and the provided taxonomy about different BCI-spellers can be helpful, as it could identify suitable systems for first-hand users, as well as opportunities of development and learning from previous studies for BCI researchers.

Brain-Computer Interface Spellers: A Review.

Steady state visual evoked potentials (SSVEPs)-based Brain-Computer Interfaces (BCIs) can provide hand-free human interaction with the environment. In the presented study, visual stimuli were displayed on Epson Moverio BT-200 augmented reality glasses, which can be easily used in smart homes. QR codes were used to identify the devices to be controlled with the BCI. In order to simulate a real life scenario, participants were instructed to go out of the lab to get a coffee. During this task light switches, elevator and a coffee machine were controlled by focusing on SSVEP stimuli displayed on the smart glasses. An average accuracy of 85.70% was achieved, which suggests that augmented reality may be used together with SSVEP to control external devices.

SSVEP-Based BCI in a Smart Home Scenario

Brain-Computer Interfaces (BCIs) allow humans to form a physical symbiosis with computer systems. One use case scenario of BCIs is communication by brain activity. High spelling speeds have been achieved with BCIs based on code-modulated visual evoked potentials (c-VEPs). Typically, the flickering stimuli are presented on a standard 60 Hz monitor. Users can find VEP-based BCIs annoying and tiring due to the perceptible flickering. This is especially the case for multi-target systems designed for maximal communication speed. Higher monitor refresh rates allow a faster flickering rate for BCI targets, and thus a more subtle visual stimulation. In this paper, user friendliness and speed of c-VEP BCIs with different monitor refresh rates (60, 120 and 200 Hz) are compared. The experiment was comprised of three sessions (each consisting of training and spelling stages), one for each tested monitor refresh rate. Performance was assessed with ITR and accuracy and user friendliness was evaluated using a questionnaire. High flickering speed is usually accompanied by poorer BCI performance. In this study, the system utilizing the 200 Hz refresh rate surprisingly competed well in terms of ITR and accuracy. Regarding user friendliness it was preferred by most users, as expected.

Effects of Monitor Refresh Rates on c-VEP BCIs

Brain-Computer Interfaces (BCIs) allow communication and control of the environment without the use of peripheral muscles. One of the standard BCI paradigms is based on steady state visual evoked potentials (SSVEPs), brain signals induced by gazing at a constantly flickering target. In this article, a VR SSVEP-based steering simulation is presented and evaluated in comparison to a standard desktop version. Three control classes were used to control the application. The experimental task was to steer a virtual vacuum robot and collect 10 dust piles (for this, at least 31 command classifications were required). Participants were instructed to complete the task twice, using a head mounted display (HMD) and the laptop screen for visual stimulation. All participants were able to complete the task in both scenarios. Mean accuracies of 98.91% and 97.48% and mean ITRs of 23.96 and 20.71 bits/min were achieved for the HMD and desktop control, respectively. On average, the number of commands needed to complete the task in the online experiment was 32.00 and 32.75, for the HMD and desktop scenario, respectively.

SSVEP-Based BCI in Virtual Reality - Control of a Vacuum Cleaner Robot

Brain-Computer Interface (BCI) provides a non-muscular communication by using the brain signals. During last decades, the BCI-systems provided various graphical user interfaces, especially the speller interfaces. Efforts had been made to increase the speller speed and user friendliness of the system. In this paper, we are introducing a browser-driven SSVEP-based BCI web speller (accessible at: https://bci-lab.hochschule-rhein-waal.de/en/speller/). This web speller can be used with the help of major existing web browsers. Thus, all the international researchers working in the field of BCI can access this web speller online free of charge, and they can run this speller using their own classifier applications. In the presented three-step web speller, the user can select a desired character by going through three different steps of the web interface. The browser-driven BCI speller was tested in this study by ten subjects, who were asked to spell the words "BCI_LAB" and "KLEVE". All of the subjects were able to perform the spelling tasks, with the mean accuracy of 94.5% and an average information transfer rate (ITR) of 12.7 bits/min.

A Browser-Driven SSVEP-Based BCI Web Speller

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