Brain-computer interface (BCI), an emerging technology that facilitates communication between brain and computer, has attracted a great deal of research in recent years. Researchers provide experimental results demonstrating that BCI can restore the capabilities of physically challenged people, hence improving the quality of their lives. BCI has revolutionized and positively impacted several industries, including entertainment and gaming, automation and control, education, neuromarketing, and neuroergonomics. Notwithstanding its broad range of applications, the global trend of BCI remains lightly discussed in the literature. Understanding the trend may inform researchers and practitioners on the direction of the field, and on where they should invest their efforts more. Noting this significance, we have analyzed 25,336 metadata of BCI publications from Scopus to determine advancement of the field. The analysis shows an exponential growth of BCI publications in China from 2019 onwards, exceeding those from the United States that started to decline during the same period. Implications and reasons for this trend are discussed. Furthermore, we have extensively discussed challenges and threats limiting exploitation of BCI capabilities. A typical BCI architecture is hypothesized to address two prominent BCI threats, privacy and security, as an attempt to make the technology commercially viable to the society. 

Brain–computer interface: trend, challenges, and threats

The constantly developing biomedical engineering field and newer and more advanced BCI (brain–computer interface) systems require their designers to constantly develop and search for various innovative methods used in their creation. In response to practical requirements and the possibility of using the system in real conditions, the authors propose an advanced solution using EEG (electroencephalography) signal analysis. A BCI system design approach using artificial intelligence for the advanced analysis of signals containing facial expressions as control commands was used. The signals were burdened with numerous artifacts caused by simulated nervous tics. The proposed expert system consisted of two neural networks. The first one allowed for the analysis of one-second samples of EEG signals from selected electrodes on the basis of power spectrum estimation waveforms. Thus, it was possible to generate an appropriate control signal as a result of appropriate facial expression commands. The second of the neural networks detected the appearance and type of nervous tics in the signal. Additionally, the participants were affected by interference such as street and TV or radio sound, Wi-Fi and radio waves. The system designed in such a way is adapted to the requirements of the everyday life of people with disabilities, in particular those in wheelchairs, whose control is based on BCI technology.

/pdf/bci-wheelchair-control-using-expert-system-classifying-eeg-1qsb28k0.pdf

BCI Wheelchair Control Using Expert System Classifying EEG Signals Based on Power Spectrum Estimation and Nervous Tics Detection

Industrial Internet of Things (IIoT) and its applications have seen a paradigm shift since the advent of artificial intelligence and machine learning. However, these methods are mostly data-centric and lack flexibility. Brain-Computer Interface (BCI) is envisioned as an efficient solution that may overcome the shortcomings of data-centric techniques by introducing human intuition in the process loop. In this work, we present an exhaustive study on the feasibility of adopting BCI techniques for industrial applications, particularly Electroencephalography (EEG). We present a comprehensive literature survey on the basics of EEG (including signal processing techniques) and its involved paradigms, together with its application scope. We identify the potential use cases of EEG-based BCI systems in industries in the current traction while accounting for their pros and cons. Additionally, we also highlight the existing challenges and propose possible off-the-shelf solutions for overcoming them. We present a two-pronged study (hardware and software specifications) on its deployment by performing lab-scale experiments with a single-channel EEG headset. We compare the metrics of our experiments to 64, 32, and 25 channel data from other studies (collected from the Internet). On average, we observe CPU usage of up to 35%, memory usage of 1.75 Gb, and data transfer rates of 25 Mbps. In contrast to the conventional setups, we demonstrate how a minimalistic setup using a single-channel EEG headset may facilitate complex applications like job inspection in manufacturing processes, with an overall accuracy of 70%. 

A Critical Survey of EEG-Based BCI Systems for Applications in Industrial Internet of Things

This review systematically explores the application of transformer-based models in EEG signal processing and brain-computer interface (BCI) development, with a distinct focus on ensuring methodological rigour and adhering to empirical validations within the existing literature. By examining various transformer architectures, such as the Temporal Spatial Transformer Network (TSTN) and EEG Conformer, this review delineates their capabilities in mitigating challenges intrinsic to EEG data, such as noise and artifacts, and their subsequent implications on decoding and classification accuracies across disparate mental tasks. The analytical scope extends to a meticulous examination of attention mechanisms within transformer models, delineating their role in illuminating critical temporal and spatial EEG features and facilitating interpretability in model decision-making processes. The discourse additionally encapsulates emerging works that substantiate the efficacy of transformer models in noise reduction of EEG signals and diversifying applications beyond the conventional motor imagery paradigm. Furthermore, this review elucidates evident gaps and propounds exploratory avenues in the applications of pre-trained transformers in EEG analysis and the potential expansion into real-time and multi-task BCI applications. Collectively, this review distils extant knowledge, navigates through the empirical findings, and puts forward a structured synthesis, thereby serving as a conduit for informed future research endeavours in transformer-enhanced, EEG-based BCI systems. 

Exploring the frontier: Transformer-based models in EEG signal analysis for brain-computer interfaces

Compared with other biometrics, such as fingerprints and irises, EEG signals contain richer identity information due to their high subject dependence. In this study, we proposed an affective EEG-based person identification by using an affective interrelated temporal–spatial transformer (AITST) to overcome the influence of different emotional states in affective EEG-based person identification. The AITST contains an interrelated temporal–spatial attention that can preserve the interrelationships of temporal and spatial domains while extracting temporal and spatial features of the EEG signal. We evaluated the proposed AITST on the public emotional database DEAP. The experimental results show that our AITST can reach an average recognition accuracy of 98.94±0.04% in the low valence, low arousal state, 99.06±0.02% in low valence, high arousal state, 98.97±0.02% in high valence, low arousal state, and 99.21±0.03% in the high valence, high arousal state, significantly outperforming the other compared models and overcoming the influence of different emotional states. Furthermore, we explored the contribution of different frequency bands, different emotional states and different features of EEG for EEG-based person identification. The results showed that gamma band of EEG, power spectrum density (PSD), and high arousal state have the greatest contribution to affective EEG-based person identification. Source code is available at https://github.com/JerryKingQAQ/AITST. 

AITST - Affective EEG-based person identification via interrelated temporal-spatial transformer

In the last few decades, Brain-Computer Interface (BCI) research has focused predominantly on clinical applications, notably to enable severely disabled people to interact with the environment. However, recent studies rely mostly on the use of non-invasive electroencephalographic (EEG) devices, suggesting that BCI might be ready to be used outside laboratories. In particular, Industry 4.0 is a rapidly evolving sector that aims to restructure traditional methods by deploying digital tools and cyber-physical systems. BCI-based solutions are attracting increasing attention in this field to support industrial performance by optimizing the cognitive load of industrial operators, facilitating human-robot interactions, and make operations in critical conditions more secure. Although these advancements seem promising, numerous aspects must be considered before developing any operational solutions. Indeed, the development of novel applications outside optimal laboratory conditions raises many challenges. In the current study, we carried out a detailed literature review to investigate the main challenges and present criteria relevant to the future deployment of BCI applications for Industry 4.0.

/pdf/toward-eeg-based-bci-applications-for-industry-4-0-5c6eubbegq.pdf

Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications.

Recent years have been marked by the fulgurant expansion of non-invasive Brain-Computer Interface (BCI) devices and applications in various contexts (medical, industrial etc.). This technology allows agents "to directly act with thoughts," bypassing the peripheral motor system. Interestingly, it is worth noting that typical non-invasive BCI paradigms remain distant from neuroscientific models of human voluntary action. Notably, bidirectional links between action and perception are constantly ignored in BCI experiments. In the current perspective article, we proposed an innovative BCI paradigm that is directly inspired by the ideomotor principle, which postulates that voluntary actions are driven by the anticipated representation of forthcoming perceptual effects. We believe that (1) adapting BCI paradigms could allow simple action-effect bindings and consequently action-effect predictions and (2) using neural underpinnings of those action-effect predictions as features of interest in AI methods, could lead to more accurate and naturalistic BCI-mediated actions.

/pdf/theoretical-perspective-on-an-ideomotor-brain-computer-1kukyndwn1.pdf

Theoretical Perspective on an Ideomotor Brain-Computer Interface: Toward a Naturalistic and Non-invasive Brain-Computer Interface Paradigm Based on Action-Effect Representation.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Solène Le Bars

Author Tools

Chat about Author

Papers

Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications.

Theoretical Perspective on an Ideomotor Brain-Computer Interface: Toward a Naturalistic and Non-invasive Brain-Computer Interface Paradigm Based on Action-Effect Representation.