Wearable Brain-computer Interface Research Articles

Integration of Augmented Reality and Brain-Computer Interface Technologies for Health Care Applications: Exploratory and Prototyping Study.

BackgroundAugmented reality (AR) and brain-computer interface (BCI) are promising technologies that have a tremendous potential to revolutionize health care. While there has been a growing interest in these technologies for medical applications in the recent years, the combined use of AR and BCI remains a fairly unexplored area that offers significant opportunities for improving health care professional education and clinical practice. This paper describes a recent study to explore the integration of AR and BCI technologies for health care applications.ObjectiveThe described effort aims to advance an understanding of how AR and BCI technologies can effectively work together to transform modern health care practice by providing new mechanisms to improve patient and provider learning, communication, and shared decision-making.MethodsThe study methods included an environmental scan of AR and BCI technologies currently used in health care, a use case analysis for a combined AR-BCI capability, and development of an integrated AR-BCI prototype solution for health care applications.ResultsThe study resulted in a novel interface technology solution that enables interoperability between consumer-grade wearable AR and BCI devices and provides the users with an ability to control digital objects in augmented reality using neural commands. The article discusses this novel solution within the context of practical digital health use cases developed during the course of the study where the combined AR and BCI technologies are anticipated to produce the most impact.ConclusionsAs one of the pioneering efforts in the area of AR and BCI integration, the study presents a practical implementation pathway for AR-BCI integration and provides directions for future research and innovation in this area.

Development of a flexible dry electrode based MXene with low contact impedance for biopotential recording

The dry electrode for biopotential recording is urgently need in wearable health monitoring systems, brain-computer interface (BCI), and human–computer interaction (HCI). However, high contact impedances of the electrode and the skin and inadequate shape adaptability of the electrode are challenges for the dry electrode to record signals accurately. Here, we present a flexible dry electrode based on transferring MXene (Ti3C2Tx) ink on a flexible polyimide (PI) substrate for biopotential recording. The electrode exhibits low contact impedances and excellent flexibility to conform the skin well. In vivo biopotential recording demonstrated the flexible dry electrode can record electrocardiograms (ECG), electroencephalograms (EEG), and electrooculogram (EOG) as accurately as the standard electrode (Ag/AgCl electrode). Moreover, our electrode can record ECG accurately, even the subject is in motion. The flexible dry electrode is suitable for wearable biopotential recording and promising to be used in wearable health monitoring systems, BCI, and HCI.

Visual and Haptic Feedback in Detecting Motor Imagery within a Wearable Brain-Computer Interface

Visual and Haptic Feedback in Detecting Motor Imagery within a Wearable Brain-Computer Interface

Metrological Characterization of Consumer-Grade Equipment for Wearable Brain–Computer Interfaces and Extended Reality

This article proposes the characterization of stimulation and detection equipment in the design of wearable brain–computer interfaces (BCIs) based on visually evoked potentials. In particular, methods for the characterization of the optical output of commercial smart glasses and for the calibration of a low-cost electroencephalograph (EEG) are presented and discussed. The reported results show that, by proper characterization and calibration, consumer-grade equipment is suitable for BCI applications. Moreover, unexpected harmonics of the visual stimuli have to be taken into account by suitable processing. Finally, the EEG shows an adequate linear behavior, but its gain error of around 10% should be corrected by calibration in some applications.

Demonstrating the Viability of Mapping Deep Learning Based EEG Decoders to Spiking Networks on Low-powered Neuromorphic Chips.

Accurate and low-power decoding of brain signals such as electroencephalography (EEG) is key to constructing brain-computer interface (BCI) based wearable devices. While deep learning approaches have progressed substantially in terms of decoding accuracy, their power consumption is relatively high for mobile applications. Neuromorphic hardware arises as a promising solution to tackle this problem since it can run massive spiking neural networks with energy consumption orders of magnitude lower than traditional hardware. Herein, we show the viability of directly mapping a continuous-valued convolutional neural network for motor imagery EEG classification to a spiking neural network. The converted network, able to run on the SpiNNaker neuromorphic chip, only shows a 1.91% decrease in accuracy after conversion. Thus, we take full advantage of the benefits of both deep learning accuracies and low-power neuro-inspired hardware, properties that are key for the development of wearable BCI devices.

Challenges and applications of wearable brain-computer interface technologies

By enabling direct communication between the brain and external devices, neurotechnologies will eventually be integrated into nearly every aspect of everyday life.

A Wearable Brain-Computer Interface Instrument with Aug- Mented Reality-Based Interface for General Applications

In the project we are demonstrating the combined usage Augmented Reality(AR) and brain faced com- puter interface(BI) which can be used to control the robotic acuratorby.Thismethod is more simple and more user friendly. Here brainwave senor will work in its normal setting detecting alpha, beta, and gam- ma signals. These signals are decoded to detect eye movements. These are very limited on its own since the number of combinations possible to make higher andmorecomplextaskpossible.Asasolutiontothis AR is integrated with the BCI application to make control interface more user friendly. Thisapplication can be used in many cases including many robotic anddevicecontrollingcases.HereweuseBCI-ARto detect eye paralysis that can be archive by detecting eyelidmovementofpersonbywearingheadbend.

A Wearable Brain-Computer Interface Instrument with Aug- Mented Reality-Based Interface for General Applications

In the project we are demonstrating the combined usage Augmented Reality(AR) and brain faced com- puter interface(BI) which can be used to control the robotic acurator by. This method is more simple and more user friendly. Here brainwave senor will work in its normal setting detecting alpha, beta, and gam- ma signals. These signals are decoded to detect eye movements. These are very limited on its own since the number of combinations possible to make higher and more complex task possible. Asa solution to this AR is integrated with the BCI application to make control interface more user friendly. This application can be used in many cases including many robotic and device controlling cases. Here we use BCI-AR to detect eye paralysis that can be archive by detecting eye lid movement of person by wearing head bend.

Vehicle driver drowsiness detection method using wearable EEG based on convolution neural network.

Vehicle drivers driving cars under the situation of drowsiness can cause serious traffic accidents. In this paper, a vehicle driver drowsiness detection method using wearable electroencephalographic (EEG) based on convolution neural network (CNN) is proposed. The presented method consists of three parts: data collection using wearable EEG, vehicle driver drowsiness detection and the early warning strategy. Firstly, a wearable brain computer interface (BCI) is used to monitor and collect the EEG signals in the simulation environment of drowsy driving and awake driving. Secondly, the neural networks with Inception module and modified AlexNet module are trained to classify the EEG signals. Finally, the early warning strategy module will function and it will sound an alarm if the vehicle driver is judged as drowsy. The method was tested on driving EEG data from simulated drowsy driving. The results show that using neural network with Inception module reached 95.59% classification accuracy based on one second time window samples and using modified AlexNet module reached 94.68%. The simulation and test results demonstrate the feasibility of the proposed drowsiness detection method for vehicle driving safety.

An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces.

Brain-computer interfaces (BCIs) provide humans a new communication channel by encoding and decoding brain activities. Steady-state visual evoked potential (SSVEP)-based BCI stands out among many BCI paradigms because of its non-invasiveness, little user training, and high information transfer rate (ITR). However, the use of conductive gel and bulky hardware in the traditional Electroencephalogram (EEG) method hinder the application of SSVEP-based BCIs. Besides, continuous visual stimulation in long time use will lead to visual fatigue and pose a new challenge to the practical application. This study provides an open dataset, which is collected based on a wearable SSVEP-based BCI system, and comprehensively compares the SSVEP data obtained by wet and dry electrodes. The dataset consists of 8-channel EEG data from 102 healthy subjects performing a 12-target SSVEP-based BCI task. For each subject, 10 consecutive blocks were recorded using wet and dry electrodes, respectively. The dataset can be used to investigate the performance of wet and dry electrodes in SSVEP-based BCIs. Besides, the dataset provides sufficient data for developing new target identification algorithms to improve the performance of wearable SSVEP-based BCIs.

In-Ear Electrode EEG for Practical SSVEP BCI

Steady State Visual Evoked Potential (SSVEP) methods for brain–computer interfaces (BCI) are popular due to higher information transfer rate and easier setup with minimal training, compared to alternative methods. With precisely generated visual stimulus frequency, it is possible to translate brain signals into external actions or signals. Traditionally, SSVEP data is collected from the occipital region using electrodes with or without gel, normally mounted on a head cap. In this experimental study, we develop an in-ear electrode to collect SSVEP data for four different flicker frequencies and compare against occipital scalp electrode data. Data from five participants demonstrates the feasibility of in-ear electrode based SSVEP, significantly enhancing the practicability of wearable BCI applications.

Wearable Brain–Computer Interface Instrumentation for Robot-Based Rehabilitation by Augmented Reality

An instrument for remote control of the robot by wearable brain–computer interface (BCI) is proposed for rehabilitating children with attention-deficit/hyperactivity disorder (ADHD). Augmented reality (AR) glasses generate flickering stimuli, and a single-channel electroencephalographic BCI detects the elicited steady-state visual evoked potentials (SSVEPs). This allows benefiting from the SSVEP robustness by leaving available the view of robot movements. Together with the lack of training, a single channel maximizes the device’s wearability, fundamental for the acceptance by ADHD children. Effectively controlling the movements of a robot through a new channel enhances rehabilitation engagement and effectiveness. A case study at an accredited rehabilitation center on ten healthy adult subjects highlighted an average accuracy higher than 83%, with information transfer rate (ITR) up to 39 b/min. Preliminary further tests on four ADHD patients between six- and eight-years old provided highly positive feedback on device acceptance and attentional performance.

MEG Sensor Selection for Neural Speech Decoding.

Direct decoding of speech from the brain is a faster alternative to current electroencephalography (EEG) speller-based brain-computer interfaces (BCI) in providing communication assistance to locked-in patients. Magnetoencephalography (MEG) has recently shown great potential as a non-invasive neuroimaging modality for neural speech decoding, owing in part to its spatial selectivity over other high-temporal resolution devices. Standard MEG systems have a large number of cryogenically cooled channels/sensors (200 – 300) encapsulated within a fixed liquid helium dewar, precluding their use as wearable BCI devices. Fortunately, recently developed optically pumped magnetometers (OPM) do not require cryogens, and have the potential to be wearable and movable making them more suitable for BCI applications. This design is also modular allowing for customized montages to include only the sensors necessary for a particular task. As the number of sensors bears a heavy influence on the cost, size, and weight of MEG systems, minimizing the number of sensors is critical for designing practical MEG-based BCIs in the future. In this study, we sought to identify an optimal set of MEG channels to decode imagined and spoken phrases from the MEG signals. Using a forward selection algorithm with a support vector machine classifier we found that nine optimally located MEG gradiometers provided higher decoding accuracy compared to using all channels. Additionally, the forward selection algorithm achieved similar performance to dimensionality reduction using a stacked-sparse-autoencoder. Analysis of spatial dynamics of speech decoding suggested that both left and right hemisphere sensors contribute to speech decoding. Sensors approximately located near Broca’s area were found to be commonly contributing among the higher-ranked sensors across all subjects.

A Wearable Brain–Computer Interface Instrument for Augmented Reality-Based Inspection in Industry 4.0

This paper proposes a wearable monitoring system for inspection in the framework of Industry 4.0. The instrument integrates augmented reality (AR) glasses with a noninvasive single-channel brain–computer interface (BCI), which replaces the classical input interface of AR platforms. Steady-state visually evoked potentials (SSVEP) are measured by a single-channel electroencephalography (EEG) and simple power spectral density analysis. The visual stimuli for SSVEP elicitation are provided by AR glasses while displaying the inspection information. The real-time metrological performance of the BCI is assessed by the receiver operating characteristic curve on the experimental data from 20 subjects. The characterization was carried out by considering stimulation times from 10.0 down to 2.0 s. The thresholds for the classification were found to be dependent on the subject and the obtained average accuracy goes from 98.9% at 10.0 s to 81.1% at 2.0 s. An inspection case study of the integrated AR-BCI device shows encouraging accuracy of about 80% of lab values.

High-Speed Visual Target Identification for Low-Cost Wearable Brain-Computer Interfaces

Non-invasive brain-computer interfaces (BCI) have received a great deal of attention due to recent advances in signal processing. Two types of electroencephalograms (EEG), P300 and steady-state visual evoked potential (SSVEP), have been widely used to enable paralyzed patients to communicate with others. Although there have been many signal processing algorithms focusing on target identification accuracies such as power spectral density analysis (PSDA) and canonical correlation analysis (CCA), their high computational complexity drives up the cost of such systems. In the proposed lightweight target identification algorithm, we have focused on developing an improved information transfer rate (ITR) for high-quality communication and reducing overall implementation cost. The proposed algorithm, CCA-Lite, includes two algorithmic optimizations–signal binarization and on-the-fly covariance matrix calculation–which have enabled the development of a low-cost, single-channel, and wearable BCI system using SSVEP. The prototypical BCI system makes use of an ARM Cortex-M3-based low-cost microcontroller unit (MCU), which has been built for 1.5s SSVEP recordings. Compared to the state-of-the-art CCA-based target identification algorithm, CCA-Lite exhibits 25% better ITR and has reduced memory requirements by 92% and single-target identification cycle time by 26%.

A Wearable SSVEP-Based BCI System for Quadcopter Control Using Head-Mounted Device

Restoring the interaction between disabled people and the 3-D physical world via a brain-computer interface (BCI) is an exciting topic. To this end, we designed a wearable BCI system based on the steady-state visual evoked potential (SSVEP), which enables 3-D navigation of quadcopter flight with immersive first-person visual feedback using a head-mounted device. In addition, to alleviate the user’s operational burden, this paper provides asynchronous switch control for the users. The transitional state due to head movement in an asynchronous BCI was isolated online and translated into hover to eliminate its influence. The experimental results in the physical environment showed that the subjects could accomplish the 3-D flight tasks accurately and smoothly using our system. In particular, in this paper, we proposed an information transfer rate metric that is suitable for the asynchronous task. We demonstrated the feasibility of using the head-mounted device and a proper control strategy to facilitate the portability and practicability of the SSVEP-based BCI system for its navigation utility.

Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG.

The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS) for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using a wireless wearable electroencephalograph (EEG) is presented. This system is designed to detect whether the driver is drowsiness. The proposed system consists of three main parts: (1) a wireless wearable EEG collection; (2) train driver vigilance detection; and (3) early warning device for train driver. In the first part, an 8-channel wireless wearable brain-computer interface (BCI) device acquires the locomotive driver’s brain EEG signal comfortably under high-speed train-driving conditions. The recorded data are transmitted to a personal computer (PC) via Bluetooth. In the second step, a support vector machine (SVM) classification algorithm is implemented to determine the vigilance level using the Fast Fourier transform (FFT) to extract the EEG power spectrum density (PSD). In addition, an early warning device begins to work if fatigue is detected. The simulation and test results demonstrate the feasibility of the proposed fatigue detection system for high-speed train safety.

A Brain-Computer Interface Based on a Few-Channel EEG-fNIRS Bimodal System

With the development of the wearable brain-computer interface (BCI), a few-channel BCI system is necessary for its application to daily life. In this paper, we proposed a bimodal BCI system that uses only a few channels of electroencephalograph (EEG) and functional near-infrared spectroscopy (fNIRS) signals to obtain relatively high accuracy. We developed new approaches for signal acquisition and signal processing to improve the performance of this few-channel BCI system. At the signal acquisition stage, source analysis was applied for both EEG and fNIRS signals to select the optimal channels for bimodal signal collection. At the feature extraction stage, phase-space reconstruction was applied to the selected three-channel EEG signals to expand them into multichannel signals, thus allowing the use of the traditional effective common spatial pattern to extract EEG features. For the fNIRS signal, the Hurst exponents for the selected ten channels were calculated and composed of the fNIRS data feature. At the classification stage, EEG and fNIRS features were joined and classified with the support vector machine. The averaged classification accuracy of 12 participants was 81.2% for the bimodal EEG-fNIRS signals, which was significantly higher than that for either single modality.

A Vehicle Active Safety Model: Vehicle Speed Control Based on Driver Vigilance Detection Using Wearable EEG and Sparse Representation.

In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver vigilance detection, and vehicle speed control strategy. First of all, a homemade low-cost comfortable wearable brain-computer interface (BCI) system with eight channels is designed for collecting the driver’s EEG signal. Second, wavelet de-noising and down-sample algorithms are utilized to enhance the quality of EEG data, and Fast Fourier Transformation (FFT) is adopted to extract the EEG power spectrum density (PSD). In this step, sparse representation classification combined with k-singular value decomposition (KSVD) is firstly introduced in PSD to estimate the driver’s vigilance level . Finally, a novel safety strategy of vehicle speed control, which controls the electronic throttle opening and automatic braking after driver fatigue detection using the above method, is presented to avoid serious collisions and traffic accidents. The simulation and practical testing results demonstrate the feasibility of the vehicle active safety model.

A Wearable Channel Selection-Based Brain-Computer Interface for Motor Imagery Detection.

Motor imagery-based brain-computer interface (BCI) is a communication interface between an external machine and the brain. Many kinds of spatial filters are used in BCIs to enhance the electroencephalography (EEG) features related to motor imagery. The approach of channel selection, developed to reserve meaningful EEG channels, is also an important technique for the development of BCIs. However, current BCI systems require a conventional EEG machine and EEG electrodes with conductive gel to acquire multi-channel EEG signals and then transmit these EEG signals to the back-end computer to perform the approach of channel selection. This reduces the convenience of use in daily life and increases the limitations of BCI applications. In order to improve the above issues, a novel wearable channel selection-based brain-computer interface is proposed. Here, retractable comb-shaped active dry electrodes are designed to measure the EEG signals on a hairy site, without conductive gel. By the design of analog CAR spatial filters and the firmware of EEG acquisition module, the function of spatial filters could be performed without any calculation, and channel selection could be performed in the front-end device to improve the practicability of detecting motor imagery in the wearable EEG device directly or in commercial mobile phones or tablets, which may have relatively low system specifications. Finally, the performance of the proposed BCI is investigated, and the experimental results show that the proposed system is a good wearable BCI system prototype.