Special Issue "The Challenges in Brain-Computer Interface (BCI) - toward Practical BCI"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 July 2020).

Special Issue Editors

Dr. Han-Jeong Hwang
Website
Guest Editor
Department of Electronics and Information Engineering, Korea University
Interests: brain–computer Interface (BCI); neuromodulation; myoelectric control; deep learning; machine learning
Special Issues and Collections in MDPI journals
Dr. Chang-Hee Han
Website
Guest Editor
Department of Software Engineering and Theoretical Computer Science, Technical University of Berlin, Berlin, Germany
Interests: brain-computer interface (BCI), machine learning, EEG/NIRS hybrid brain signal analysis

Special Issue Information

Dear Colleagues,

For the last several decades, the brain–computer interface (BCI) has been intensively studied to establish a novel method of communication using brain activity for those who are paralyzed but have intact brain functions. However, the performance and reliability of BCI technologies are still limited due to various challenging issues, such as the nonstationarity nature of brain activity, session-to-session transfer, physiological artifacts contained in brain activity, and so on. Consequently, clinically available BCI systems have rarely been introduced to date.

This Special Issue aims to share the current state-of-the-art trends and future directions in the BCI field, thereby encouraging the development of practical solutions to tackle the aforementioned challenging issues. We invite researchers to submit original research articles, clinical studies, and review/survey articles that contribute to the advance of BCI technologies based on non-invasive neuroimaging modalities, i.e., electroencephalography (EEG) and near-infrared spectroscopy (NIRS). This Special Issue will focus in the challenges in practical BCI, including but not limited to:

  • Enhanced BCI Performance: Development of new devices, algorithms, and paradigms;
  • Reliable BCI: Evaluation of test-retest reliability and session-to-session transfer based on multiday datasets;
  • Ambulatory BCI: Development of portable, easy-to-use, and wireless EEG/NIRS recording systems and their related methodologies;
  • Neuromodulation-based BCI: Use of electrical and magnetic brain stimulation to improve the performance and reliability of BCI systems;
  • Practical BCI: Development of BCI applications, e.g., rehabilitation, entertainment, drowsiness detection, emotion decoding;
  • Development of new artifact rejection algorithms for EOG, EMG, ECG, etc.;
  • Releasing publicly available BCI datasets;
  • Review/survey articles.

Dr. Han-Jeong Hwang
Dr. Chang-Hee Han
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (11 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

Open AccessArticle
Use of Both Eyes-Open and Eyes-Closed Resting States May Yield a More Robust Predictor of Motor Imagery BCI Performance
Electronics 2020, 9(4), 690; https://doi.org/10.3390/electronics9040690 - 23 Apr 2020
Cited by 3
Abstract
Motor-imagery brain-computer interface (MI-BCI) is a technique that manipulates external machines using brain activities, and is highly useful to amyotrophic lateral sclerosis patients who cannot move their limbs. However, it is reported that approximately 15–30% of users cannot modulate their brain signals, which [...] Read more.
Motor-imagery brain-computer interface (MI-BCI) is a technique that manipulates external machines using brain activities, and is highly useful to amyotrophic lateral sclerosis patients who cannot move their limbs. However, it is reported that approximately 15–30% of users cannot modulate their brain signals, which results in the inability to operate motor imagery BCI systems. Thus, advance prediction of BCI performance has drawn researchers’ attention, and some predictors have been proposed using the alpha band’s power, as well as other spectral bands’ powers, or spectral entropy from resting state electroencephalography (EEG). However, these predictors rely on a single state alone, such as the eyes-closed or eyes-open state; thus, they may often be less stable or unable to explain inter-/intra-subject variability. In this work, a modified predictor of MI-BCI performance that considered both brain states (eyes-open and eyes-closed resting states) was investigated with 41 online MI-BCI session datasets acquired from 15 subjects. The results showed that our proposed predictor and online MI-BCI classification accuracy were positively and highly significantly correlated (r = 0.71, p < 0.1 × 10 7 ), which indicates that the use of multiple brain states may yield a more robust predictor than the use of a single state alone. Full article
Show Figures

Figure 1

Open AccessArticle
Motor Imagery Based Continuous Teleoperation Robot Control with Tactile Feedback
Electronics 2020, 9(1), 174; https://doi.org/10.3390/electronics9010174 - 17 Jan 2020
Cited by 5
Abstract
Brain computer interface (BCI) adopts human brain signals to control external devices directly without using normal neural pathway. Recent study has explored many applications, such as controlling a teleoperation robot by electroencephalography (EEG) signals. However, utilizing the motor imagery EEG-based BCI to perform [...] Read more.
Brain computer interface (BCI) adopts human brain signals to control external devices directly without using normal neural pathway. Recent study has explored many applications, such as controlling a teleoperation robot by electroencephalography (EEG) signals. However, utilizing the motor imagery EEG-based BCI to perform teleoperation for reach and grasp task still has many difficulties, especially in continuous multidimensional control of robot and tactile feedback. In this research, a motor imagery EEG-based continuous teleoperation robot control system with tactile feedback was proposed. Firstly, mental imagination of different hand movements was translated into continuous command to control the remote robotic arm to reach the hover area of the target through a wireless local area network (LAN). Then, the robotic arm automatically completed the task of grasping the target. Meanwhile, the tactile information of remote robotic gripper was detected and converted to the feedback command. Finally, the vibrotactile stimulus was supplied to users to improve their telepresence. Experimental results demonstrate the feasibility of using the motor imagery EEG acquired by wireless portable equipment to realize the continuous teleoperation robot control system to finish the reach and grasp task. The average two-dimensional continuous control success rates for online Task 1 and Task 2 of the six subjects were 78.0% ± 6.1% and 66.2% ± 6.0%, respectively. Furthermore, compared with the traditional EEG triggered robot control using the predefined trajectory, the continuous fully two-dimensional control can not only improve the teleoperation robot system’s efficiency but also give the subject a more natural control which is critical to human–machine interaction (HMI). In addition, vibrotactile stimulus can improve the operator’s telepresence and task performance. Full article
Show Figures

Figure 1

Open AccessArticle
Optimal Feature Search for Vigilance Estimation Using Deep Reinforcement Learning
Electronics 2020, 9(1), 142; https://doi.org/10.3390/electronics9010142 - 11 Jan 2020
Abstract
A low level of vigilance is one of the main reasons for traffic and industrial accidents. We conducted experiments to evoke the low level of vigilance and record physiological data through single-channel electroencephalogram (EEG) and electrocardiogram (ECG) measurements. In this study, a deep [...] Read more.
A low level of vigilance is one of the main reasons for traffic and industrial accidents. We conducted experiments to evoke the low level of vigilance and record physiological data through single-channel electroencephalogram (EEG) and electrocardiogram (ECG) measurements. In this study, a deep Q-network (DQN) algorithm was designed, using conventional feature engineering and deep convolutional neural network (CNN) methods, to extract the optimal features. The DQN yielded the optimal features: two CNN features from ECG and two conventional features from EEG. The ECG features were more significant for tracking the transitions within the alertness continuum with the DQN. The classification was performed with a small number of features, and the results were similar to those from using all of the features. This suggests that the DQN could be applied to investigating biomarkers for physiological responses and optimizing the classification system to reduce the input resources. Full article
Show Figures

Figure 1

Open AccessArticle
Simultaneous Decoding of Eccentricity and Direction Information for a Single-Flicker SSVEP BCI
Electronics 2019, 8(12), 1554; https://doi.org/10.3390/electronics8121554 - 17 Dec 2019
Cited by 1
Abstract
The feasibility of a steady-state visual evoked potential (SSVEP) brain–computer interface (BCI) with a single-flicker stimulus for multiple-target decoding has been demonstrated in a number of recent studies. The single-flicker BCIs have mainly employed the direction information for encoding the targets, i.e., different [...] Read more.
The feasibility of a steady-state visual evoked potential (SSVEP) brain–computer interface (BCI) with a single-flicker stimulus for multiple-target decoding has been demonstrated in a number of recent studies. The single-flicker BCIs have mainly employed the direction information for encoding the targets, i.e., different targets are placed at different spatial directions relative to the flicker stimulus. The present study explored whether visual eccentricity information can also be used to encode targets for the purpose of increasing the number of targets in the single-flicker BCIs. A total number of 16 targets were encoded, placed at eight spatial directions, and two eccentricities (2.5° and 5°) relative to a 12 Hz flicker stimulus. Whereas distinct SSVEP topographies were elicited when participants gazed at targets of different directions, targets of different eccentricities were mainly represented by different signal-to-noise ratios (SNRs). Using a canonical correlation analysis-based classification algorithm, simultaneous decoding of both direction and eccentricity information was achieved, with an offline 16-class accuracy of 66.8 ± 16.4% averaged over 12 participants and a best individual accuracy of 90.0%. Our results demonstrate a single-flicker BCI with a substantially increased target number towards practical applications. Full article
Show Figures

Figure 1

Open AccessArticle
Open-Access fNIRS Dataset for Classification of Unilateral Finger- and Foot-Tapping
Electronics 2019, 8(12), 1486; https://doi.org/10.3390/electronics8121486 - 06 Dec 2019
Cited by 3
Abstract
Numerous open-access electroencephalography (EEG) datasets have been released and widely employed by EEG researchers. However, not many functional near-infrared spectroscopy (fNIRS) datasets are publicly available. More fNIRS datasets need to be freely accessible in order to facilitate fNIRS studies. Toward this end, we [...] Read more.
Numerous open-access electroencephalography (EEG) datasets have been released and widely employed by EEG researchers. However, not many functional near-infrared spectroscopy (fNIRS) datasets are publicly available. More fNIRS datasets need to be freely accessible in order to facilitate fNIRS studies. Toward this end, we introduce an open-access fNIRS dataset for three-class classification. The concentration changes of oxygenated and reduced hemoglobin were measured, while 30 volunteers repeated each of the three types of overt movements (i.e., left- and right-hand unilateral complex finger-tapping, foot-tapping) for 25 times. The ternary support vector machine (SVM) classification accuracy obtained using leave-one-out cross-validation was estimated at 70.4% ± 18.4% on average. A total of 21 out of 30 volunteers scored a superior binary SVM classification accuracy (left-hand vs. right-hand finger-tapping) of over 80.0%. We believe that the introduced fNIRS dataset can facilitate future fNIRS studies. Full article
Show Figures

Figure 1

Open AccessArticle
Brain Computer Interface-Based Action Observation Game Enhances Mu Suppression in Patients with Stroke
Electronics 2019, 8(12), 1466; https://doi.org/10.3390/electronics8121466 - 02 Dec 2019
Cited by 2
Abstract
Action observation (AO), based on the mirror neuron theory, is a promising strategy to promote motor cortical activation in neurorehabilitation. Brain computer interface (BCI) can detect a user’s intention and provide them with brain state-dependent feedback to assist with patient rehabilitation. We investigated [...] Read more.
Action observation (AO), based on the mirror neuron theory, is a promising strategy to promote motor cortical activation in neurorehabilitation. Brain computer interface (BCI) can detect a user’s intention and provide them with brain state-dependent feedback to assist with patient rehabilitation. We investigated the effects of a combined BCI-AO game on power of mu band attenuation in stroke patients. Nineteen patients with subacute stroke were recruited. A BCI-AO game provided real-time feedback to participants regarding their attention to a flickering action video using steady-state visual-evoked potentials. All participants watched a video of repetitive grasping actions under two conditions: (1) BCI-AO game and (2) conventional AO, in random order. In the BCI-AO game, feedback on participants’ observation scores and observation time was provided. In conventional AO, a non-flickering video and no feedback were provided. The magnitude of mu suppression in the central motor, temporal, parietal, and occipital areas was significantly higher in the BCI-AO game than in the conventional AO. The magnitude of mu suppression was significantly higher in the BCI-AO game than in the conventional AO both in the affected and unaffected hemispheres. These results support the facilitatory effects of the BCI-AO game on mu suppression over conventional AO. Full article
Show Figures

Figure 1

Open AccessArticle
Deep-ACTINet: End-to-End Deep Learning Architecture for Automatic Sleep-Wake Detection Using Wrist Actigraphy
Electronics 2019, 8(12), 1461; https://doi.org/10.3390/electronics8121461 - 02 Dec 2019
Cited by 1
Abstract
Sleep scoring is the first step for diagnosing sleep disorders. A variety of chronic diseases related to sleep disorders could be identified using sleep-state estimation. This paper presents an end-to-end deep learning architecture using wrist actigraphy, called Deep-ACTINet, for automatic sleep-wake detection using [...] Read more.
Sleep scoring is the first step for diagnosing sleep disorders. A variety of chronic diseases related to sleep disorders could be identified using sleep-state estimation. This paper presents an end-to-end deep learning architecture using wrist actigraphy, called Deep-ACTINet, for automatic sleep-wake detection using only noise canceled raw activity signals recorded during sleep and without a feature engineering method. As a benchmark test, the proposed Deep-ACTINet is compared with two conventional fixed model based sleep-wake scoring algorithms and four feature engineering based machine learning algorithms. The datasets were recorded from 10 subjects using three-axis accelerometer wristband sensors for eight hours in bed. The sleep recordings were analyzed using Deep-ACTINet and conventional approaches, and the suggested end-to-end deep learning model gained the highest accuracy of 89.65%, recall of 92.99%, and precision of 92.09% on average. These values were approximately 4.74% and 4.05% higher than those for the traditional model based and feature based machine learning algorithms, respectively. In addition, the neuron outputs of Deep-ACTINet contained the most significant information for separating the asleep and awake states, which was demonstrated by their high correlations with conventional significant features. Deep-ACTINet was designed to be a general model and thus has the potential to replace current actigraphy algorithms equipped in wristband wearable devices. Full article
Show Figures

Figure 1

Open AccessArticle
Decoding EEG in Motor Imagery Tasks with Graph Semi-Supervised Broad Learning
Electronics 2019, 8(11), 1273; https://doi.org/10.3390/electronics8111273 - 01 Nov 2019
Abstract
In recent years, the accurate and real-time classification of electroencephalogram (EEG) signals has drawn increasing attention in the application of brain-computer interface technology (BCI). Supervised methods used to classify EEG signals have gotten satisfactory results. However, unlabeled samples are more frequent than labeled [...] Read more.
In recent years, the accurate and real-time classification of electroencephalogram (EEG) signals has drawn increasing attention in the application of brain-computer interface technology (BCI). Supervised methods used to classify EEG signals have gotten satisfactory results. However, unlabeled samples are more frequent than labeled samples, so how to simultaneously utilize limited labeled samples and many unlabeled samples becomes a research hotspot. In this paper, we propose a new graph-based semi-supervised broad learning system (GSS-BLS), which combines the graph label propagation method to obtain pseudo-labels and then trains the GSS-BLS classifier together with other labeled samples. Three BCI competition datasets are used to assess the GSS-BLS approach and five comparison algorithms: BLS, ELM, HELM, LapSVM and SMIR. The experimental results show that GSS-BLS achieves satisfying Cohen’s kappa values in three datasets. GSS-BLS achieves the better results of each subject in the 2-class and 4-class datasets and has significant improvements compared with original BLS except subject C6. Therefore, the proposed GSS-BLS is an effective semi-supervised algorithm for classifying EEG signals. Full article
Show Figures

Figure 1

Open AccessArticle
Online Home Appliance Control Using EEG-Based Brain–Computer Interfaces
Electronics 2019, 8(10), 1101; https://doi.org/10.3390/electronics8101101 - 30 Sep 2019
Cited by 3
Abstract
Brain–computer interfaces (BCIs) allow patients with paralysis to control external devices by mental commands. Recent advances in home automation and the Internet of things may extend the horizon of BCI applications into daily living environments at home. In this study, we developed an [...] Read more.
Brain–computer interfaces (BCIs) allow patients with paralysis to control external devices by mental commands. Recent advances in home automation and the Internet of things may extend the horizon of BCI applications into daily living environments at home. In this study, we developed an online BCI based on scalp electroencephalography (EEG) to control home appliances. The BCI users controlled TV channels, a digital door-lock system, and an electric light system in an unshielded environment. The BCI was designed to harness P300 and N200 components of event-related potentials (ERPs). On average, the BCI users could control TV channels with an accuracy of 83.0% ± 17.9%, the digital door-lock with 78.7% ± 16.2% accuracy, and the light with 80.0% ± 15.6% accuracy, respectively. Our study demonstrates a feasibility to control multiple home appliances using EEG-based BCIs. Full article
Show Figures

Figure 1

Open AccessArticle
A Dry Electrode Cap and Its Application in a Steady-State Visual Evoked Potential-Based Brain–Computer Interface
Electronics 2019, 8(10), 1080; https://doi.org/10.3390/electronics8101080 - 23 Sep 2019
Cited by 1
Abstract
The wearable electroencephalogram (EEG) dry electrode acquisition system has shown great application prospects in mental state monitoring, the brain–computer interface (BCI), and other fields due to advantages such as being small in volume, light weight, and a ready-to-use facility. This study demonstrates a [...] Read more.
The wearable electroencephalogram (EEG) dry electrode acquisition system has shown great application prospects in mental state monitoring, the brain–computer interface (BCI), and other fields due to advantages such as being small in volume, light weight, and a ready-to-use facility. This study demonstrates a novel EEG cap with concise structure, easy adjustment size, as well as independently adjustable electrodes. The cap can be rapidly worn and adjusted in both horizontal and vertical dimensions. The dry electrodes on it can be adjusted independently to fit the scalp as quickly as possible. The accuracy of the BCI test employing this device is higher than when employing a headband. The proposed EEG cap makes adjustment easier and the contact impedance of the dry electrodes more uniform. Full article
Show Figures

Figure 1

Review

Jump to: Research

Open AccessFeature PaperReview
Brain-Switches for Asynchronous Brain–Computer Interfaces: A Systematic Review
Electronics 2020, 9(3), 422; https://doi.org/10.3390/electronics9030422 - 02 Mar 2020
Cited by 4
Abstract
A brain–computer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever [...] Read more.
A brain–computer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever the user wants. However, the relatively low performance of an asynchronous BCI system is problematic because redundant BCI commands are required to correct false-positive operations. To significantly reduce the number of false-positive operations of an asynchronous BCI system, a two-step approach has been proposed using a brain-switch that first determines whether the user wants to use an asynchronous BCI system before the operation of the asynchronous BCI system. This study presents a systematic review of the state-of-the-art brain-switch techniques and future research directions. To this end, we reviewed brain-switch research articles published from 2000 to 2019 in terms of their (a) neuroimaging modality, (b) paradigm, (c) operation algorithm, and (d) performance. Full article
Show Figures

Figure 1

Back to TopTop