BCI Research Research Articles

Connectivity study on resting-state EEG between motor imagery BCI-literate and BCI-illiterate groups.

Although Motor Imagery-based Brain-Computer Interface (MI-BCI) holds significant potential, its practical application faces challenges such as BCI-illiteracy. To mitigate this issue, researchers have attempted to predict BCI-illiteracy by using the resting state, as this was found to be associated with BCI performance. As connectivity's significance in neuroscience has grown, BCI researchers have applied connectivity to it. However, the issues of connectivity have not been considered fully. First, although various connectivity metrics exist, only some have been used to predict BCI-illiteracy. This is problematic because each metric has a distinct hypothesis and perspective to estimate connectivity, resulting in different outcomes according to the metric. Second, the frequency range affects the connectivity estimation. In addition, it is still unknown whether each metric has its own optimal frequency range. Third, the way that estimating connectivity may vary depending upon the dataset has not been investigated. Meanwhile, we still do not know a great deal about how the resting state EEG network differs between BCI-literacy and -illiteracy. To address the issues above, we analysed three large public EEG datasets using three functional connectivity (FC) and three effective connectivity (EC) metrics by employing diverse graph theory measures. Our analysis revealed that the appropriate frequency range to predict BCI-illiteracy varies depending upon the metric. The alpha range was found to be suitable for the metrics of the frequency domain, while alpha + theta were found to be appropriate for Multivariate Granger Causality (MVGC). The difference in network efficiency between BCI-literate and -illiterate groups was constant regardless of the metrics and datasets used. Although we observed that BCI-literacy had stronger connectivity, no other significant constructional differences were found. Based upon our findings, we predicted MI-BCI performance for the entire dataset. We discovered that combining several graph features could improve the prediction's accuracy.

What is the most important innovation in neurosurgical practice that will emerge in the next 10 years?

Brain computer interfaces BCIs are automated neurostimulators that analyse brain signals and translate them into output. They aim to allow modulation of neurological functioning at cellular and network levels without the need for clinician-performed adjustments. BCIs are beginning to progress from the research and development phase to their introduction into clinical neurosurgery. Some anticipated applications include advanced treatment of cerebral palsy and stroke, aiming to restore function to these patients. In time, they will become one of the most significant innovations in neurosurgery. 
 Provided here is a review of BCI research to date and their implementation into clinical neuroscience, aiming to highlight the importance of BCIs as a developing treatment modality in neurosurgery in the next decade. 
 A review of the literature was performed using Pubmed as the primary database. BCI applications, recent technological advancements and current challenges facing these devices were reviewed. Trends in research and development were considered and are discussed. It was found that significant developments have been made in manufacturing BCIs that can interpret cortical processes with adequate spatial and temporal resolution. It was shown that the number of procedures to implant neuromodulators taking place in the UK each year is increasing, with more funding provided for BCI research each year. Further to this, a review of publication data found that neuromodulation devices placed among the top new patent categories in neurosurgery in recent years, a large portion of which were BCIs. These results highlight the continually growing interest in BCI research and their application in neurosurgery. 
 In conclusion, BCIs will become an increasingly important area of innovation in neurosurgery. Advances in translational neuroscience, imaging technologies and signal analysis will provide clearer insights into disease processes, allowing increasingly complex devices to address current limitations in the treatment of these conditions.

The ethical significance of user-control in AI-driven speech-BCIs: a narrative review.

AI-driven brain-computed interfaces aimed at restoring speech for individuals living with locked-in-syndrome are paired with ethical implications for user's autonomy, privacy and responsibility. Embedding options for sufficient levels of user-control in speech-BCI design has been proposed to mitigate these ethical challenges. However, how user-control in speech-BCIs is conceptualized and how it relates to these ethical challenges is underdetermined. In this narrative literature review, we aim to clarify and explicate the notion of user-control in speech-BCIs, to better understand in what way user-control could operationalize user's autonomy, privacy and responsibility and explore how such suggestions for increasing user-control can be translated to recommendations for the design or use of speech-BCIs. First, we identified types of user control, including executory control that can protect voluntariness of speech, and guidance control that can contribute to semantic accuracy. Second, we identified potential causes for a loss of user-control, including contributions of predictive language models, a lack of ability for neural control, or signal interference and external control. Such a loss of user control may have implications for semantic accuracy and mental privacy. Third we explored ways to design for user-control. While embedding initiation signals for users may increase executory control, they may conflict with other aims such as speed and continuity of speech. Design mechanisms for guidance control remain largely conceptual, similar trade-offs in design may be expected. We argue that preceding these trade-offs, the overarching aim of speech-BCIs needs to be defined, requiring input from current and potential users. Additionally, conceptual clarification of user-control and other (ethical) concepts in this debate has practical relevance for BCI researchers. For instance, different concepts of inner speech may have distinct ethical implications. Increased clarity of such concepts can improve anticipation of ethical implications of speech-BCIs and may help to steer design decisions.

Robotic Motion Control via P300-based Brain-Computer Interface System

BCI have ignited extensive research interest in fields such as neuroscience, artificial intelligence, and biomedical engineering, as they offer an opportunity to interact directly with the external environment brain signals. Despite the immense potential for applications, practical use of BCI still faces several challenges, including equipment cost and operational complexity. This study aims to develop a Brain-Computer Interface system based on P300 visual stimuli, utilizing a low-cost, user-friendly portable Muse EEG equipment for data acquisition. We designed and implemented a P300 visual stimulator in a 3x3 grid pattern, acquire the user's EEG signals using the Muse EEG equipment, and classify the data using a SVM classifier, ultimately realizing control over robot movement. Offline experimental results demonstrated an accuracy of 84.1% for the classifier under offline stage, while online stage achieved a successful execution rate of 81.2%. These findings substantiate the feasibility and potential of using low-cost, portable devices like the Muse EEG equipment for BCI research, opening new avenues in the field.

MI brain-computer interfaces: A concise overview

BCI stands for brain–computer interface, and this is the hottest topic among the research community to convert brain impulses into preset instructions that can be used to interact with each other or operate external equipment. A variety of BCI systems have been created during the last decades to give an alternate communications platform for people with extreme neurological illnesses including “amyotrophic lateral sclerosis, spinal cord damage, and brainstem stroke”. BCI literature reviews have also been produced to give BCI researchers well-organized information on important BCI research topics as well as valuable suggestions for the effective design of BCI systems. However, no one has lately looked into general trends in a range of EEG-based BCI study features. The inability of an EEG to measure neural activity down beyond the top layers of the brain is one of its limitations. Additionally, they have low signal-to-noise ratios and are useless for pinpointing the precise location of medications and neurotransmitters in the brain. In this paper, a literature review is conducted with 60 research appears collected from different international journals, published during different years (2013–2022). To be more valuable, this survey provides information about different aspects like signal acquisition techniques, EEG-based BCI paradigms, and channel selection techniques applied in the collected papers. A brief review of various techniques for feature extraction and classification is also included. In addition, the performance of each of the works in terms of accuracy, sensitivity, and kappa coefficient as well is manifest.

Design and Validation of a Low-Cost Mobile EEG-Based Brain-Computer Interface.

Objective: We designed and validated a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain-computer (BCI) interface and internet-of-things (IoT) applications. Approach: The EEG-based BCI headset was designed from commercial off-the-shelf (COTS) components using a multi-pronged approach that balanced interoperability, cost, portability, usability, form factor, reliability, and closed-loop operation. Main Results: The adjustable headset was designed to accommodate 90% of the population. A patent-pending self-positioning dry electrode bracket allowed for vertical self-positioning while parting the user's hair to ensure contact of the electrode with the scalp. In the current prototype, five EEG electrodes were incorporated in the electrode bracket spanning the sensorimotor cortices bilaterally, and three skin sensors were included to measure eye movement and blinks. An inertial measurement unit (IMU) provides monitoring of head movements. The EEG amplifier operates with 24-bit resolution up to 500 Hz sampling frequency and can communicate with other devices using 802.11 b/g/n WiFi. It has high signal-to-noise ratio (SNR) and common-mode rejection ratio (CMRR) (121 dB and 110 dB, respectively) and low input noise. In closed-loop BCI mode, the system can operate at 40 Hz, including real-time adaptive noise cancellation and 512 MB of processor memory. It supports LabVIEW as a backend coding language and JavaScript (JS), Cascading Style Sheets (CSS), and HyperText Markup Language (HTML) as front-end coding languages and includes training and optimization of support vector machine (SVM) neural classifiers. Extensive bench testing supports the technical specifications and human-subject pilot testing of a closed-loop BCI application to support upper-limb rehabilitation and provides proof-of-concept validation for the device's use at both the clinic and at home. Significance: The usability, interoperability, portability, reliability, and programmability of the proposed wireless closed-loop BCI system provides a low-cost solution for BCI and neurorehabilitation research and IoT applications.

Brain-Computer Interface-Based Procedure for The Improvement of Autism

Brain-computer interface refers to the computer-based system that acquires neural signals in human brains, analyses and translates them into data and commands that can be further studies via algorithm. These features introduce new opportunities in the treatment of neuropsychological disorders, opening the door to many advances in the field. Although there have been many pharmacological and counselling therapies for psychological disorders over the last few decades, we are not aware of any complete cure programme that can cure some spontaneous neurological disorders such as autism (ASD). In contrast, there are new breakthroughs and significant advances in BCI technology, which uses external stimulation and guidance to treat internal neuronal problems. This paper highlights research conducted to provide knowledge on the application of BCI-based interventions for Memory, recognition, understanding, cooperation, correspondence, and emotional development. The application of non-invasive treatment of autism for BCI types is investigated based on representative, and latest research in the field. The paper also discusses the progress and further improvement of BCI research, while investigating different BCI paradigms, and the long-term effects of this technology. The future directions for researching BCI-based applications are discussed for reconstruction and mentoring of patients with ASD.

Does Real-Time Feedback Improve User Performance in SSVEP-based Brain-Computer Interfaces?

Offline and online experiments are both widely used in SSVEP-based BCI research and development for different purposes. One of the major differences between offline and online experiments is the existence of real-time feedback to the user while they are using the interface. However, the role of feedback in SSVEP-based BCIs has not yet been well studied. This work focuses on understanding the effect of feedback in SSVEP-based BCIs and if there exists any relationship between offline and online BCI performance. An experiment was designed to compare directly the accuracies of the BCI with and without feedback for participants. Results showed that feedback can improve performance in a complex task, but no clear improvement was observed in a simple task.

Intelligent Extraction of Salient Feature From Electroencephalogram Using Redundant Discrete Wavelet Transform.

At present, electroencephalogram (EEG) signals play an irreplaceable role in the diagnosis and treatment of human diseases and medical research. EEG signals need to be processed in order to reduce the adverse effects of irrelevant physiological process interference and measurement noise. Wavelet transform (WT) can provide a time-frequency representation of a dynamic process, and it has been widely utilized in salient feature analysis of EEG. In this paper, we investigate the problem of translation variability (TV) in discrete wavelet transform (DWT), which causes degradation of time-frequency localization. It will be verified through numerical simulations that TV is caused by downsampling operations in decomposition process of DWT. The presence of TV may cause severe distortions of features in wavelet subspaces. However, this phenomenon has not attracted much attention in the scientific community. Redundant discrete wavelet transform (RDWT) is derived by eliminating the downsampling operation. RDWT enjoys the attractive merit of translation invariance. RDWT shares the same time-frequency pattern with that of DWT. The discrete delta impulse function is used to test the time-frequency response of DWT and RDWT in wavelet subspaces. The results show that DWT is very sensitive to the translation of delta impulse function, while RDWT keeps the decomposition results unchanged. This conclusion has also been verified again in decomposition of actual EEG signals. In conclusion, to avoid possible distortions of features caused by translation sensitivity in DWT, we recommend the use of RDWT with more stable performance in BCI research and clinical applications.

An SSVEP-based BCI with LEDs visual stimuli using dynamic window CCA algorithm

The improvements of the visual stimulus paradigm and the target recognition algorithm in the (steady-state visual evoked potential) SSVEP-based (brain computer interface) BCI have attracted widespread attention among BCI researchers. The LEDs visual stimulus paradigm was adopted in this paper, which is more portable and lighter than the widely used computer screen visual stimulus paradigm. In terms of algorithms, the ‘dynamic window’ strategy was introduced in canonical correlation analysis (CCA). The dynamic window CCA can automatically find the optimal window length of data in the recognition process, and this could increase the information transfer rate (ITR) of the system. An offline experiment with LEDs visual stimulus paradigm was designed to evaluate the performance of dynamic window CCA. The results showed that the LEDs visual stimulus paradigm is feasible in the SSVEP-based BCI, and the dynamic window CCA got the higher ITR compared with CCA. In this paper, an online experiment was also conducted using dynamic window CCA. The online SSVEP system used the LEDs visual stimulus paradigm to control an artificial hand. The results showed that the dynamic window CCA is well suited for online systems.

What Internal Variables Affect Sensorimotor Rhythm Brain-Computer Interface (SMR-BCI) Performance?

Description In this review article, we aimed to create a summary of the effects of internal variables on the performance of sensorimotor rhythm-based brain computer interfaces (SMR-BCIs). SMR-BCIs can be potentially used for interfacing between the brain and devices, bypassing usual central nervous system output, such as muscle activity. The careful consideration of internal factors, affecting SMR-BCI performance, can maximize BCI application in both healthy and disabled people. Internal variables may be generalized as descriptors of the processes mainly dependent on the BCI user and/or originating within the user. The current review aimed to critically evaluate and summarize the currently accumulated body of knowledge regarding the effect of internal variables on SMR-BCI performance. The examples of such internal variables include motor imagery, hand coordination, attention, motivation, quality of life, mood and neurophysiological signals other than SMR. We will conclude our review with the discussion about the future developments regarding the research on the effects of internal variables on SMR-BCI performance. The end-goal of this review paper is to provide current BCI users and researchers with the reference guide that can help them optimize the SMR-BCI performance by accounting for possible influences of various internal factors.

A study of classification techniques on P300 speller dataset

Communication is the basic need of humans to interact with their surroundings. BCI acts as a great tool for providing the means of basic communication to people with locked-in syndrome. For such patients, these basic things matter most and give them a sense of independence. Communication through BCI can be done using various paradigms, P300 speller is one of these paradigms in which the user is instructed to focus their attention on the desired character and able to produce negative and positive slow cortical potential changes which is then interpreted by the BCI system. Although P300 based BCI was introduced over twenty years ago, the past few years have seen a rapid increase in P300 based BCI research. In this paper, we overview the current status of P300 BCI technology and also discussed and compare different approaches using Random Forest, Support Vector Machine (SVM), and Extreme Gradient Boosting (XgBoost) classification algorithm.

A 36-Class Bimodal ERP Brain-Computer Interface Using Location-Congruent Auditory-Tactile Stimuli.

To date, traditional visual-based event-related potential brain-computer interface (ERP-BCI) systems continue to dominate the mainstream BCI research. However, these conventional BCIs are unsuitable for the individuals who have partly or completely lost their vision. Considering the poor performance of gaze independent ERP-BCIs, it is necessary to study techniques to improve the performance of these BCI systems. In this paper, we developed a novel 36-class bimodal ERP-BCI system based on tactile and auditory stimuli, in which six-virtual-direction audio files produced via head related transfer functions (HRTF) were delivered through headphones and location-congruent electro-tactile stimuli were simultaneously delivered to the corresponding position using electrodes placed on the abdomen and waist. We selected the eight best channels, trained a Bayesian linear discriminant analysis (BLDA) classifier and acquired the optimal trial number for target selection in online process. The average online information transfer rate (ITR) of the bimodal ERP-BCI reached 11.66 bit/min, improvements of 35.11% and 36.69% compared to the auditory (8.63 bit/min) and tactile approaches (8.53 bit/min), respectively. The results demonstrate the performance of the bimodal system is superior to each unimodal system. These facts indicate that the proposed bimodal system has potential utility as a gaze-independent BCI in future real-world applications.

Chapter 18 - Electroencephalography

The electroencephalogram (EEG) was invented almost 100 years ago and is still a method of choice for many research questions, even applications-from functional brain imaging in neuroscientific investigations during movement to real-time applications like brain-computer interfacing. This chapter gives some background information on the establishment and properties of the EEG. This chapter starts with a closer look at the sources of EEG at a micro or neuronal level, followed by recording techniques, types of electrodes, and common EEG artifacts. Then an overview on EEG phenomena, namely, spontaneous EEG and event-related potentials build the middle part of this chapter. The last part discusses brain signals, which are used in current BCI research, including short descriptions and examples of applications.

Connectivity steered graph Fourier transform for motor imagery BCI decoding

Objective. Graph signal processing (GSP) concepts are exploited for brain activity decoding and particularly the detection and recognition of a motor imagery (MI) movement. A novel signal analytic technique that combines graph Fourier transform (GFT) with estimates of cross-frequency coupling (CFC) and discriminative learning is introduced as a means to recover the subject’s intention from the multichannel signal. Approach. Adopting a multi-view perspective, based on the popular concept of co-existing and interacting brain rhythms, a multilayer network model is first built from empirical data and its connectivity graph is used to derive the GFT-basis. A personalized decoding scheme supporting a binary decision, either ‘left versus right’ or ‘rest versus MI’, is crafted from a small set of training trials. Electroencephalographic (EEG) activity from 12 volunteers recorded during two randomly alternating, externally cued, MI tasks (clenching either left or right fist) and a rest condition is used to introduce and validate our methodology. In addition, the introduced methodology was further validated based on dataset IVa of BCI III competition. Main results. Our GFT-domain decoding scheme achieves nearly optimal performance and proves superior to alternative techniques that are very popular in the field. Significance. At a conceptual level, our work suggests a fruitful way to introduce network neuroscience in BCI research. At a more practical level, it is characterized by efficiency. Training is realized using a small number of exemplar trials and decoding requires very simple operations that leaves room for real-time implementation.

Stereoscopic Motion Perception Research Based on Steady-state Visual Motion Evoked Potential.

The combination of BCI technology and stereoscopic three-dimensional (3D) display has gradually become a trend, while stereo-based BCI has been used in rehabilitation training and medical testing. However, present stereo-based BCI research mainly stays in the static stereo environment, and the main method of visual stimulation is flickering, which does not effectively utilize the characteristic of the stereoscopic display technology. Therefore, we proposed a novel stimulation method based on stereoscopic motion. It utilized the stereo reciprocating motion of the plane of intensive line to elicit steady-state visual motion evoked potential (SSMVEP). The results shown that the correlation canonical analysis (CCA) coefficients of the EEG signal of stereoscopic motion (4.3 Hz-6.3 Hz) was significant higher than the non-stereoscopic motion, and more brain areas were activated. This stimulation method can induce significant visual response and has a great potential in the application of virtual reality stereo-based BCI system.

Asilomar survey: researcher perspectives on ethical principles and guidelines for BCI research

ABSTRACTBrain-computer Interface (BCI) research is rapidly expanding, and it engages domains of human experience that many find central to our current understanding of ourselves. Ethical principles or guidelines can provide researchers with tools to engage in ethical reflection and to address practical problems in research. Though researchers have called for clearer ethical principles or guidelines, there is little existing data on what form these should take. We developed a prospective set of ethical principles for BCI research with specific guidelines and shared them, via a survey, to participants at the 6th International BCI Meeting at the Asilomar Conference Center in 2016. Respondents were broadly supportive of principles of Care for Subjects, Modesty, Participation, Inclusivity, Relationality, Justice, and Social Impact. Principles more traditionally aligned with responsible conduct of research showed higher levels of endorsement. Researcher support for specific principle-based ethical guidelines varied with respect to stringency of researcher obligations.

Guidelines for Feature Matching Assessment of Brain-Computer Interfaces for Augmentative and Alternative Communication.

Brain-computer interfaces (BCIs) can provide access to augmentative and alternative communication (AAC) devices using neurological activity alone without voluntary movements. As with traditional AAC access methods, BCI performance may be influenced by the cognitive-sensory-motor and motor imagery profiles of those who use these devices. Therefore, we propose a person-centered, feature matching framework consistent with clinical AAC best practices to ensure selection of the most appropriate BCI technology to meet individuals' communication needs. The proposed feature matching procedure is based on the current state of the art in BCI technology and published reports on cognitive, sensory, motor, and motor imagery factors important for successful operation of BCI devices. Considerations for successful selection of BCI for accessing AAC are summarized based on interpretation from a multidisciplinary team with experience in AAC, BCI, neuromotor disorders, and cognitive assessment. The set of features that support each BCI option are discussed in a hypothetical case format to model possible transition of BCI research from the laboratory into clinical AAC applications. This procedure is an initial step toward consideration of feature matching assessment for the full range of BCI devices. Future investigations are needed to fully examine how person-centered factors influence BCI performance across devices.

Creamino: A Cost-Effective, Open-Source EEG-Based BCI System.

This paper presents an open source framework called Creamino. It consists of an Arduino-based cost-effective quick-setup EEG platform built with off-the-shelf components and a set of software modules that easily allow users to connect this system to Simulink or BCI-oriented tools (such as BCI2000 or OpenViBE) and set up a wide number of neuroscientific experiments. Creamino is capable of processing multiple EEG channels in real-time and operates under Windows, Linux, and Mac OS X in real-time on a standard PC. Its objective is to provide a system that can be readily fabricated and used for neurophysiological experiments and, at the same time, can serve as the basis for development of novel BCI platforms by accessing and modifying its open source hardware and software libraries. Schematics, gerber files, bill of materials, source code, software modules, demonstration videos, and instructions on how to use these modules are available free of charge for research and educational purposes online at https://github.com/ArcesUnibo/creamino. Application cases show how the system can be used for neuroscientific or BCI experiments. Thanks to its low production cost and its compatibility with open-source BCI tools, the system presented is particularly suitable for use in BCI research and educational applications.

Classification of Multi-Class BCI Data by Common Spatial Pattern and Fuzzy System

Improving classification accuracy of motor imagery-based brain computer interface (MI-BCI) systems has been discussed widely in the BCI research community. Analyses of multi-class MI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to binary-class data. This paper introduces a new model for multi-class MI-BCI data classification. The approach encompasses two main stages: feature extraction and fuzzy classification. In the feature extraction phase, a common spatial pattern algorithm is employed to extract significant discriminant features from the multi-class data. The obtained features serve as inputs to a fuzzy classifier that is designed by fusing the fuzzy standard additive model and particle swarm optimization method. The proposed fuzzy model aims to handle uncertainty, noise, and outliers existed in MI-BCI data. Experimental studies are carried out using two benchmark BCI competition data sets. Our results show the effectiveness of the fuzzy classifier against its competing techniques. The proposed model paths the way for paralysed or disordered patients to use movement imagery to control different assistive devices in their daily activities.