Brain-computer Interface Research Research Articles

MetaBCI: An open-source platform for brain–computer interfaces

Background:Recently, brain–computer interfaces (BCIs) have attracted worldwide attention for their great potential in clinical and real-life applications. To implement a complete BCI system, one must set up several links to translate the brain intent into computer commands. However, there is not an open-source software platform that can cover all links of the BCI chain. Method:This study developed a one-stop open-source BCI software, namely MetaBCI, to facilitate the construction of a BCI system. MetaBCI is written in Python, and has the functions of stimulus presentation (Brainstim), data loading and processing (Brainda), and online information flow (Brainflow). This paper introduces the detailed information of MetaBCI and presents four typical application cases. Results:The results showed that MetaBCI was an extensible and feature-rich software platform for BCI research and application, which could effectively encode, decode, and feedback brain activities. Conclusions:MetaBCI can greatly lower the BCI’s technical threshold for BCI beginners and can save time and cost to build up a practical BCI system. The source code is available at https://github.com/TBC-TJU/MetaBCI, expecting new contributions from the BCI community.

Brain computer interfaces in computer science and engineering areas: a systematic study

<span>Brain-computer interfaces (BCI) are a channel that implements direct communication between the brain and some external unit. Developments of BCIs can provide new application opportunities in a large number of fields of use. In the development of BCI devices, the development of technology and digital technology represented a big change, as it provided the necessary computing power to implement and run the continuously developing signal processing algorithms that ensure processing and evaluation. The aim of this paper is to provide an overview of BCI research results which were published in the engineering field. In the present study, articles that had a greater impact, where the annual average number of citations is greater than 30, in the BCI field were reviewed and processed in a systematic way, in order to make individual research more comparable. The systematic processing was focused on the aims of application, used device/ dataset, applied data process and achieved best accuracy. This systematic study summarizes the most effective methods used in the BCI processing and highlights the future trends. The results showed an accuracy of 85% thanks to increasingly reliable, accurate and cost-effective signal detection and processing devices, as well as algorithms.</span>

CNN-Based Image Analysis for EEG Signal Characterization.

This article focuses on an attempt to classify and recognize the characterized images of EEG signals directly. For EEG signals, the recognition and judgment of different signals has been the key direction of research. CNN (Convolutional Neural Network) models are usually used for recognition of EEG raw signals about movement and Imagery Dataset. However, the images of EEG raw signals are basically unreadable for researchers, so characterization is a common tool. However, direct recognition of the characterized images is a relatively empty area in the existing research because it requires much higher machine performance than the traditional raw signal recognition. However, feeding the extracted feature images into a CNN and training them can be an efficient and intuitive response to the potential of EEG for brain mapping. The main goal of this research is to examine the discriminative capabilities of traditional visual and image neural networks for pictures described by EEG data. This is not typical in contemporary brain-computer interface research. The direct recognition of the described photos uses a lot of GPU (graphics computing unit) resources, but for the characterized images are easier for people to read than the original images. This work indicates the viability of direct research on defined pictures and increases the application scenario of EEG signals.

A Combined Virtual Electrode-Based ESA and CNN Method for MI-EEG Signal Feature Extraction and Classification.

A Brain-Computer Interface (BCI) is a medium for communication between the human brain and computers, which does not rely on other human neural tissues, but only decodes Electroencephalography (EEG) signals and converts them into commands to control external devices. Motor Imagery (MI) is an important BCI paradigm that generates a spontaneous EEG signal without external stimulation by imagining limb movements to strengthen the brain's compensatory function, and it has a promising future in the field of computer-aided diagnosis and rehabilitation technology for brain diseases. However, there are a series of technical difficulties in the research of motor imagery-based brain-computer interface (MI-BCI) systems, such as: large individual differences in subjects and poor performance of the cross-subject classification model; a low signal-to-noise ratio of EEG signals and poor classification accuracy; and the poor online performance of the MI-BCI system. To address the above problems, this paper proposed a combined virtual electrode-based EEG Source Analysis (ESA) and Convolutional Neural Network (CNN) method for MI-EEG signal feature extraction and classification. The outcomes reveal that the online MI-BCI system developed based on this method can improve the decoding ability of multi-task MI-EEG after training, it can learn generalized features from multiple subjects in cross-subject experiments and has some adaptability to the individual differences of new subjects, and it can decode the EEG intent online and realize the brain control function of the intelligent cart, which provides a new idea for the research of an online MI-BCI system.

A large EEG database with users’ profile information for motor imagery brain-computer interface research

We present and share a large database containing electroencephalographic signals from 87 human participants, collected during a single day of brain-computer interface (BCI) experiments, organized into 3 datasets (A, B, and C) that were all recorded using the same protocol: right and left hand motor imagery (MI). Each session contains 240 trials (120 per class), which represents more than 20,800 trials, or approximately 70 hours of recording time. It includes the performance of the associated BCI users, detailed information about the demographics, personality profile as well as some cognitive traits and the experimental instructions and codes (executed in the open-source platform OpenViBE). Such database could prove useful for various studies, including but not limited to: (1) studying the relationships between BCI users’ profiles and their BCI performances, (2) studying how EEG signals properties varies for different users’ profiles and MI tasks, (3) using the large number of participants to design cross-user BCI machine learning algorithms or (4) incorporating users’ profile information into the design of EEG signal classification algorithms.

Meditation as an effective BCI training protocol for controlling virtual wheeled robots

Background: Brain-Computer Interface (BCI) systems allow the use of electroencephalographic activities to control devices. As such, it can be an important tool for disabled people since using a BCI does not involves any muscular stimulation. Despite its great potential as an assistive technology, BCI systems are yet scarcely available outside scientific contexts. The main reason is that BCI is still not accurate enough for real world situations. In this paper, we investigated if mindfulness meditation can help BCI users to perform better, particularly on controlling virtual wheeled robots. Controlling virtual wheeled robots must be seen as an important BCI research issue, since disabled people can take the best advantages from assistive wheeled robots. Methods: Case-control study with 30 subjects, meditators and non-meditators, who controlled a virtual wheeled robot using a BCI system. Results: In straight-ahead moves, the robot was 30% faster when controlled by meditators. In stop-and-go moves, the meditators controlled the robot with an accuracy of 66% while the non-meditators’ accuracy was 27%. In rectangular shape moves, meditators also performed better than non-meditators, with an accuracy of 40% against 7% of non-meditators. Conclusion: The results show that meditators performed better than non-meditators. As such, we recommend combining mindfulness meditation with standard BCI training protocols for better control of virtual wheeled robots.

Priming cross-session motor imagery classification with a universal deep domain adaptation framework

Electroencephalogram (EEG) based motor imagery (MI) brain–computer interfaces (BCI) are widely used in applications related to rehabilitation and external device control. However, due to the non-stationary and low signal-to-noise ratio characteristics of EEG, classifying motor imagery tasks of the same participant from different recording sessions is generally challenging. Whether the classification accuracy of cross-session MI can be improved from the perspective of domain adaptation is a question worth verifying. In this paper, we propose a Siamese deep domain adaptation (SDDA) framework for cross-session MI classification based on mathematical models in domain adaptation theory. The SDDA framework primarily consists of three components: a novel preprocessing method based on domain-invariant features, a maximum mean discrepancy (MMD) loss for aligning source and target domain embedding features, and an improved cosine-based center loss designed to suppress the influence of noise and outliers on the neural network. The SDDA framework has been validated with two classic and popular convolutional neural networks (EEGNet and ConvNet) from BCI research field in two MI EEG public datasets (BCI Competition IV IIA, IIB). Compared with the vanilla EEGNet and ConvNet, the SDDA framework improves the MI classification accuracy by 10.49%, 7.60% respectively in IIA dataset, and 4.59%, 3.35% in IIB dataset. The SDDA not only significantly improves the classification performance of the vanilla networks but also surpasses state-of-the-art transfer learning methods, making it a superior and user-friendly approach for MI classification.

Design and verification of a wearable wireless 64-channel high-resolution EEG acquisition system with wi-fi transmission.

Brain-computer interfaces (BCIs) allow communication between the brain and the external world. This type of technology has been extensively studied. However, BCI instruments with high signal quality are typically heavy and large. Thus, recording electroencephalography (EEG) signals is an inconvenient task. In recent years, system-on-chip (SoC) approaches have been integrated into BCI research, and sensors for wireless portable devices have been developed; however, there is still considerable work to be done. As neuroscience research has advanced, EEG signal analyses have come to require more accurate data. Due to the limited bandwidth of Bluetooth wireless transmission technology, EEG measurement systems with more than 16 channels must be used to reduce the sampling rate and prevent data loss. Therefore, the goal of this study was to develop a multichannel, high-resolution (24-bit), high-sampling-rate EEG BCI device that transmits signals via Wi-Fi. We believe that this system can be used in neuroscience research. The EEG acquisition system proposed in this work is based on a Cortex-M4 microcontroller with a Wi-Fi subsystem, providing a multichannel design and improved signal quality. This system is compatible with wet sensors, Ag/AgCl electrodes, and dry sensors. A LabVIEW-based user interface receives EEG data via Wi-Fi transmission and saves the raw data for offline analysis. In previous cognitive experiments, event tags have been communicated using Recommended Standard 232 (RS-232). The developed system was validated through event-related potential (ERP) and steady-state visually evoked potential (SSVEP) experiments. Our experimental results demonstrate that this system is suitable for recording EEG measurements and has potential in practical applications. The advantages of the developed system include its high sampling rate and high amplification. Additionally, in the future, Internet of Things (IoT) technology can be integrated into the system for remote real-time analysis or edge computing.

Graph adaptive semi-supervised discriminative subspace learning for EEG emotion recognition

Since Electroencephalogram (EEG) is resistant to camouflage and contains abundant neurophysiological information, it shows significant superiorities in objective emotion recognition, making EEG-based emotion recognition become a hot research field in brain-computer interface research. However, EEG is generally non-stationary and has a low signal-to-noise ratio, which is difficult to analyze. Inspired by the consensus that exploring a discriminative subspace representation usually helps to capture the semantic information of EEG data, in this paper we propose a Graph Adaptive Semi-supervised Discriminative Subspace Learning (GASDSL) model for EEG-based emotion recognition. GASDSL aims to explore a discriminative subspace in which the intra-class scatter decreases while the inter-class separability increases. The adaptive maximum entropy graph construction and semi-supervised subspace emotional state prediction are adopted to mediate the discriminative subspace learning. Extensive comparative studies on the SEED-IV and SEED-V datasets depict that 1) GASDSL achieved satisfactory emotion recognition accuracy compared with other semi-supervised learning models, 2) the discriminative abilities of both the learned maximum entropy graph and subspace are improved as the model iterates, and 3) the features extracted from the Gamma band, the left/right temporal, prefrontal, and (central) parietal lobes contributed more to emotion recognition based on the spatial-frequency pattern analysis results.

Meditation as an effective BCI training protocol for controlling wheeled robots

Background: Brain-Computer Interface (BCI) systems allow the use of electroencephalographic activities to control devices. As such, it can be an important tool for disabled people since using a BCI does not involves any muscular stimulation. Despite its great potential as an assistive technology, BCI systems are yet scarcely available outside scientific contexts. The main reason is that BCI is still not accurate enough for real world situations. In this paper, we investigated if mindfulness meditation can help BCI users to perform better, particularly on controlling wheeled robots. Controlling wheeled robots must be seen as an important BCI research issue, since disabled people can take the best advantages from assistive wheeled robots. Methods: Case-control study with 30 subjects, meditators and non-meditators, who controlled a simulated wheeled robot using a BCI system. Results: In straight-ahead moves, the robot was 30% faster when controlled by meditators. In stop-and-go moves, the meditators controlled the robot with an accuracy of 66% while the non-meditators’ accuracy was 27%. In rectangular shape moves, meditators also performed better than non-meditators, with an accuracy of 40% against 7% of non-meditators. Conclusion: The results show that meditators performed better than non-meditators. As such, we recommend combining mindfulness meditation with standard BCI training protocols for better control of wheeled robots.

Design and implementation of high sampling rate and multichannel wireless recorder for EEG monitoring and SSVEP response detection.

The collection and process of human brain activity signals play an essential role in developing brain-computer interface (BCI) systems. A portable electroencephalogram (EEG) device has become an important tool for monitoring brain activity and diagnosing mental diseases. However, the miniaturization, portability, and scalability of EEG recorder are the current bottleneck in the research and application of BCI. For scalp EEG and other applications, the current study designs a 32-channel EEG recorder with a sampling rate up to 30 kHz and 16-bit accuracy, which can meet both the demands of scalp and intracranial EEG signal recording. A fully integrated electrophysiology microchip RHS2116 controlled by FPGA is employed to build the EEG recorder, and the design meets the requirements of high sampling rate, high transmission rate and channel extensive. The experimental results show that the developed EEG recorder provides a maximum 30 kHz sampling rate and 58 Mbps wireless transmission rate. The electrophysiological experiments were performed on scalp and intracranial EEG collection. An inflatable helmet with adjustable contact impedance was designed, and the pressurization can improve the SNR by approximately 4 times, the average accuracy of steady-state visual evoked potential (SSVEP) was 93.12%. Animal experiments were also performed on rats, and spike activity was captured successfully. The designed multichannel wireless EEG collection system is simple and comfort, the helmet-EEG recorder can capture the bioelectric signals without noticeable interference, and it has high measurement performance and great potential for practical application in BCI systems.

An open-source human-in-the-loop BCI research framework: method and design.

Brain-computer interfaces (BCIs) translate brain activity into digital commands for interaction with the physical world. The technology has great potential in several applied areas, ranging from medical applications to entertainment industry, and creates new conditions for basic research in cognitive neuroscience. The BCIs of today, however, offer only crude online classification of the user's current state of mind, and more sophisticated decoding of mental states depends on time-consuming offline data analysis. The present paper addresses this limitation directly by leveraging a set of improvements to the analytical pipeline to pave the way for the next generation of online BCIs. Specifically, we introduce an open-source research framework that features a modular and customizable hardware-independent design. This framework facilitates human-in-the-loop (HIL) model training and retraining, real-time stimulus control, and enables transfer learning and cloud computing for the online classification of electroencephalography (EEG) data. Stimuli for the subject and diagnostics for the researcher are shown on separate displays using web browser technologies. Messages are sent using the Lab Streaming Layer standard and websockets. Real-time signal processing and classification, as well as training of machine learning models, is facilitated by the open-source Python package Timeflux. The framework runs on Linux, MacOS, and Windows. While online analysis is the main target of the BCI-HIL framework, offline analysis of the EEG data can be performed with Python, MATLAB, and Julia through packages like MNE, EEGLAB, or FieldTrip. The paper describes and discusses desirable properties of a human-in-the-loop BCI research platform. The BCI-HIL framework is released under MIT license with examples at: bci.lu.se/bci-hil (or at: github.com/bci-hil/bci-hil).

Scalable, modular continuous wave functional near-infrared spectroscopy system (Spotlight).

We present a fiberless, portable, and modular continuous wave-functional near-infrared spectroscopy system, Spotlight, consisting of multiple palm-sized modules-each containing high-density light-emitting diode and silicon photomultiplier detector arrays embedded in a flexible membrane that facilitates optode coupling to scalp curvature. Spotlight's goal is to be a more portable, accessible, and powerful functional near-infrared spectroscopy (fNIRS) device for neuroscience and brain-computer interface (BCI) applications. We hope that the Spotlight designs we share here can spur more advances in fNIRS technology and better enable future non-invasive neuroscience and BCI research. We report sensor characteristics in system validation on phantoms and motor cortical hemodynamic responses in a human finger-tapping experiment, where subjects wore custom 3D-printed caps with two sensor modules. The task conditions can be decoded offline with a median accuracy of 69.6%, reaching 94.7% for the best subject, and at a comparable accuracy in real time for a subset of subjects. We quantified how well the custom caps fitted to each subject and observed that better fit leads to more observed task-dependent hemodynamic response and better decoding accuracy. The advances presented here should serve to make fNIRS more accessible for BCI applications.

Intracortical brain-computer interfaces in primates: a review and outlook.

Brain-computer interfaces (BCI) translate brain signals into artificial output to restore or replace natural central nervous system (CNS) functions. Multiple processes, including sensorimotor integration, decision-making, motor planning, execution, and updating, are involved in any movement. For example, a BCI may be better able to restore naturalistic motor behaviors if it uses signals from multiple brain areas and decodes natural behaviors' cognitive and motor aspects. This review provides an overview of the preliminary information necessary to plan a BCI project focusing on intracortical implants in primates. Since the brain structure and areas of non-human primates (NHP) are similar to humans, exploring the result of NHP studies will eventually benefit human BCI studies. The different types of BCI systems based on the target cortical area, types of signals, and decoding methods will be discussed. In addition, various successful state-of-the-art cases will be reviewed in more detail, focusing on the general algorithm followed in the real-time system. Finally, an outlook for improving the current BCI research studies will be debated.

Mental State Prediction Using Machine Learning and EEG Signal

One of the most exciting areas of computer science right now is brain-computer interface (BCI) research. A conduit for data flow between both the brain as well as an electronic device is the brain-computer interface (BCI). Researchers in several disciplines have benefited from the advancements made possible by brain-computer interfaces. Primary fields of study include healthcare and neuroergonomics. Brain signals could be used in a variety of ways to improve healthcare at every stage, from diagnosis to rehabilitation to eventual restoration. In this research, we demonstrate how to classify EEG signals of brain waves using machine learning algorithms for predicting mental health states. The XGBoost algorithm's results have an accuracy of 99.62%, which is higher than that of any other study of its kind and the best result to date for diagnosing people's mental states from their EEG signals. This discovery will aid in taking efforts [1] to predict mental state using EEG signals to the next level.

LabVIEW Instrument Aimed for the Research on Brain-Computer Interface by Enabling the Acquisition, Processing, and the Neural Networks based Classification of the Raw EEG Signal Detected by the Embedded NeuroSky Biosensor

The Brain-Computer Interface (BCI) is a scientific field aimed at helping people with neuromotor disabilities. Among the current drawbacks of BCI research is the need for a cost-effective software instrument for simple integration with portable EEG headsets, the lack of a comparative assessment approach of various techniques underlying recognizing the most precise BCI control signal –voluntary eye-blinking, and the need for EEG datasets allowing the classification of multiple voluntary eye-blinks. The proposed BCI research-related virtual instrument accomplishes the data acquisition, processing, features extraction, and the ANN-based classification of the EEG signal detected by the NeuroSky embedded biosensor. The developed software application automatically generated fifty mixtures between selected EEG rhythms and statistical features. The EEG rhythms are related to the time and frequency domains of the raw, delta, theta, alpha, beta, and gamma. The extracted statistical features contain the mean, median, standard deviation, route mean square, Kurtosis coefficient, mode, sum, skewness, maximum, and range = maximum-minimum. The results include 100 EEG datasets to classify multiple voluntary eye-blinks: 50 datasets with 4000 recordings and 50 with 800 recordings. The LabVIEW application determined the optimal ANN models for classifying the EEG temporal sequences corresponding to detecting zero, one, two, or three voluntary eye-blinks.

The military and commercial development of brain–computer interfaces: international (in)security with brain-machine teaming

ABSTRACT Brain-computer interfaces (BCIs) are an emerging dual-use technology that will affect international security in ways similar to other dual-use technologies such as artificial intelligence, autonomous weapons, and cyber tools and tactics. In this paper, I use open sources and network analyses to reveal the structure of the BCI research and development industry, which indicates that BCIs will soon be operational, and the technology will diffuse across many sectors and regions. As a result, I argue that the unique qualities of BCIs will provide incentives to engage in conflict and disrupt international security; therefore, militaries in liberal democracies should strive to achieve and maintain BCI technological superiority over authoritarian regimes and violent non-state actors.

Sensing and Stimulation Applications of Carbon Nanomaterials in Implantable Brain-Computer Interface.

Implantable brain-computer interfaces (BCIs) are crucial tools for translating basic neuroscience concepts into clinical disease diagnosis and therapy. Among the various components of the technological chain that increases the sensing and stimulation functions of implanted BCI, the interface materials play a critical role. Carbon nanomaterials, with their superior electrical, structural, chemical, and biological capabilities, have become increasingly popular in this field. They have contributed significantly to advancing BCIs by improving the sensor signal quality of electrical and chemical signals, enhancing the impedance and stability of stimulating electrodes, and precisely modulating neural function or inhibiting inflammatory responses through drug release. This comprehensive review provides an overview of carbon nanomaterials' contributions to the field of BCI and discusses their potential applications. The topic is broadened to include the use of such materials in the field of bioelectronic interfaces, as well as the potential challenges that may arise in future implantable BCI research and development. By exploring these issues, this review aims to provide insight into the exciting developments and opportunities that lie ahead in this rapidly evolving field.

Advances in brain-computer interface based on high-frequency steady-state visual evoked potential

Steady-state visual evoked potential (SSVEP) has been widely used in the research of brain-computer interface (BCI) system in recent years. The advantages of SSVEP-BCI system include high classification accuracy, fast information transform rate and strong anti-interference ability. Most of the traditional researches induce SSVEP responses in low and middle frequency bands as control signals. However, SSVEP in this frequency band may cause visual fatigue and even induce epilepsy in subjects. In contrast, high-frequency SSVEP-BCI provides a more comfortable and natural interaction despite its lower amplitude and weaker response. Therefore, it has been widely concerned by researchers in recent years. This paper summarized and analyzed the related research of high-frequency SSVEP-BCI in the past ten years from the aspects of paradigm and algorithm. Finally, the application prospect and development direction of high-frequency SSVEP were discussed and prospected.

Comparative study of EEG motor imagery classification based on DSCNN and ELM

With the popularization of brain-computer interface (BCI) technology, the research on intention recognition of motor imagery (MI) in electroencephalogram (EEG) has turned to the latest research points. The BCI-based system can provide a powerful rehabilitation guarantee for patients with stroke and spinal cord injury. However, EEG signals have certain complexity, which is easily interfered by other noises, resulting that it is still not enough to provide some better practical application scenarios. In this paper, an improved framework has been proposed through deep separation convolution neural network (DSCNN) and extreme learning machine (ELM) to address the recognition rate of patients’ motor intention. First of all, the collected one-dimensional time series EEG signals are preprocessed into a two-dimensional grid containing spatial and temporal features. Afterwards, the DSCNN is utilized to extract the temporal features and spatial features, respectively. Thirdly, the ELM classifier is utilized to classify five kinds of MI actions according to the extracted temporal and spatial features. Experimental results indicate that the presented framework can achieve an excellent intention recognition rate of 97.88% through the public EEGMMIDB datasets. Moreover, the training time was greatly shortened from 13h30min to 9h10min with a reduction rate of about 32% under the same hardware configuration, which is superior to some advanced models. Therefore, the proposed idea not only accelerates the training speed of the model, but also can boost the application research of BCI based rehabilitation efficiently.