Submit to Sensors Review for Sensors Propose a Special Issue

Journal Browser

► Journal Browser

Signal Processing for Brain–Computer Interfaces

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".

Deadline for manuscript submissions: closed (30 December 2023) | Viewed by 36685

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special issue Signal Processing for Brain–Computer Interfaces book cover image

Share This Special Issue

Special Issue Editors

Dr. Noman Naseer

E-Mail Website
Guest Editor

Department of Mechatronics and Biomedical Engineering, Air University, Islamabad, Pakistan
Interests: brain–computer interface; machine learning; deep learning; classification
Special Issues, Collections and Topics in MDPI journals

Dr. Imran Khan Niazi

E-Mail Website
Guest Editor

Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland, New Zealand
Interests: neurorehabilitation; biomedical signal processing; machine learning; applied artificial intelligence; EEG; EMG
Special Issues, Collections and Topics in MDPI journals

Dr. Hendrik Santosa

E-Mail Website
Guest Editor

Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
Interests: functional near-infrared spectroscopy (fNIRS) data processing; statistical analysis for fNIRS signal; multi-modal neuroimaging; brain-computer interface (BCI) applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

You are cordially invited to contribute to the Special Issue on “Signal Processing for Brain–Computer Interfaces”. Signal processing in brain–computer interface (BCI) systems includes noise removal, feature extraction, and classification. Noise removal algorithms aim to eliminate or reduce several different types of noise, including physiological noises, instrument noises, motion artifacts, power line noises, and noises due to interference with other devices. In feature extraction, task related features are acquired in spatial, temporal, and spectral domains using different algorithms. Finally, the goal of classification is to translate extracted features into commands for the control of external devices.

This Special Issue shall focus on state-of-the-art noise removal, feature extraction, and classification techniques to improve the accuracy, reliability, information transfer rate, and general performance of BCI systems which may be based for invasive or non-invasive methods, for example, implanted electrodes, electrocorticography (ECoG), electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and hybrid brain imaging techniques. Original research papers, clinical studies, and review papers which describe new research on signal processing for BCIs are all welcome. I look forward to your participation in this Special Issue.

Dr. Noman Naseer
Dr. Imran Khan Niazi
Dr. Hendrik Santosa
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 submissions that pass pre-check are 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. Sensors 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 2600 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.

Keywords

brain–computer interface
signal processing
EEG
fNIRS
classification
noise removal
feature extraction
machine learning
filtering techniques
deep learning

Published Papers (12 papers)

Download All Papers

Editorial

Jump to: Research, Review

3 pages, 143 KiB

Open AccessEditorial

Editorial: Signal Processing for Brain–Computer Interfaces—Special Issue

by Noman Naseer, Imran Khan Niazi and Hendrik Santosa

Sensors 2024, 24(4), 1201; https://0-doi-org.brum.beds.ac.uk/10.3390/s24041201 - 12 Feb 2024

Viewed by 766

Abstract

With the astounding ability to capture a wealth of brain signals, Brain–Computer Interfaces (BCIs) have the potential to revolutionize humans’ quality of life [...] Full article

(This article belongs to the Special Issue Signal Processing for Brain–Computer Interfaces)

Research

Jump to: Editorial, Review

24 pages, 3693 KiB

Open AccessArticle

EEG Amplitude Modulation Analysis across Mental Tasks: Towards Improved Active BCIs

by Olivier Rosanne, Alcyr Alves de Oliveira and Tiago H. Falk

Sensors 2023, 23(23), 9352; https://0-doi-org.brum.beds.ac.uk/10.3390/s23239352 - 23 Nov 2023

Viewed by 785

Abstract

Brain–computer interface (BCI) technology has emerged as an influential communication tool with extensive applications across numerous fields, including entertainment, marketing, mental state monitoring, and particularly medical neurorehabilitation. Despite its immense potential, the reliability of BCI systems is challenged by the intricacies of data collection, environmental factors, and noisy interferences, making the interpretation of high-dimensional electroencephalogram (EEG) data a pressing issue. While the current trends in research have leant towards improving classification using deep learning-based models, our study proposes the use of new features based on EEG amplitude modulation (AM) dynamics. Experiments on an active BCI dataset comprised seven mental tasks to show the importance of the proposed features, as well as their complementarity to conventional power spectral features. Through combining the seven mental tasks, 21 binary classification tests were explored. In 17 of these 21 tests, the addition of the proposed features significantly improved classifier performance relative to using power spectral density (PSD) features only. Specifically, the average kappa score for these classifications increased from 0.57 to 0.62 using the combined feature set. An examination of the top-selected features showed the predominance of the AM-based measures, comprising over 77% of the top-ranked features. We conclude this paper with an in-depth analysis of these top-ranked features and discuss their potential for use in neurophysiology. Full article

(This article belongs to the Special Issue Signal Processing for Brain–Computer Interfaces)

► Show Figures

Figure 1

14 pages, 3540 KiB

Open AccessArticle

A Symbols Based BCI Paradigm for Intelligent Home Control Using P300 Event-Related Potentials

by Faraz Akram, Ahmed Alwakeel, Mohammed Alwakeel, Mohammad Hijji and Usman Masud

Sensors 2022, 22(24), 10000; https://0-doi-org.brum.beds.ac.uk/10.3390/s222410000 - 19 Dec 2022

Cited by 3 | Viewed by 2171

Abstract

Brain-Computer Interface (BCI) is a technique that allows the disabled to interact with a computer directly from their brain. P300 Event-Related Potentials (ERP) of the brain have widely been used in several applications of the BCIs such as character spelling, word typing, wheelchair control for the disabled, neurorehabilitation, and smart home control. Most of the work done for smart home control relies on an image flashing paradigm where six images are flashed randomly, and the users can select one of the images to control an object of interest. The shortcoming of such a scheme is that the users have only six commands available in a smart home to control. This article presents a symbol-based P300-BCI paradigm for controlling home appliances. The proposed paradigm comprises of a 12-symbols, from which users can choose one to represent their desired command in a smart home. The proposed paradigm allows users to control multiple home appliances from signals generated by the brain. The proposed paradigm also allows the users to make phone calls in a smart home environment. We put our smart home control system to the test with ten healthy volunteers, and the findings show that the proposed system can effectively operate home appliances through BCI. Using the random forest classifier, our participants had an average accuracy of 92.25 percent in controlling the home devices. As compared to the previous studies on the smart home control BCIs, the proposed paradigm gives the users more degree of freedom, and the users are not only able to control several home appliances but also have an option to dial a phone number and make a call inside the smart home. The proposed symbols-based smart home paradigm, along with the option of making a phone call, can effectively be used for controlling home through signals of the brain, as demonstrated by the results. Full article

(This article belongs to the Special Issue Signal Processing for Brain–Computer Interfaces)

► Show Figures

Figure 1

15 pages, 769 KiB

Open AccessArticle

Multi-Kernel Temporal and Spatial Convolution for EEG-Based Emotion Classification

by Taweesak Emsawas, Takashi Morita, Tsukasa Kimura, Ken-ichi Fukui and Masayuki Numao

Sensors 2022, 22(21), 8250; https://0-doi-org.brum.beds.ac.uk/10.3390/s22218250 - 27 Oct 2022

Cited by 3 | Viewed by 1833

Abstract

Deep learning using an end-to-end convolutional neural network (ConvNet) has been applied to several electroencephalography (EEG)-based brain–computer interface tasks to extract feature maps and classify the target output. However, the EEG analysis remains challenging since it requires consideration of various architectural design components that influence the representational ability of extracted features. This study proposes an EEG-based emotion classification model called the multi-kernel temporal and spatial convolution network (MultiT-S ConvNet). The multi-scale kernel is used in the model to learn various time resolutions, and separable convolutions are applied to find related spatial patterns. In addition, we enhanced both the temporal and spatial filters with a lightweight gating mechanism. To validate the performance and classification accuracy of MultiT-S ConvNet, we conduct subject-dependent and subject-independent experiments on EEG-based emotion datasets: DEAP and SEED. Compared with existing methods, MultiT-S ConvNet outperforms with higher accuracy results and a few trainable parameters. Moreover, the proposed multi-scale module in temporal filtering enables extracting a wide range of EEG representations, covering short- to long-wavelength components. This module could be further implemented in any model of EEG-based convolution networks, and its ability potentially improves the model’s learning capacity. Full article

(This article belongs to the Special Issue Signal Processing for Brain–Computer Interfaces)

► Show Figures

Figure 1

19 pages, 3012 KiB

Open AccessArticle

Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers

by Sinem Burcu Erdoğan and Gülnaz Yükselen

Sensors 2022, 22(14), 5407; https://0-doi-org.brum.beds.ac.uk/10.3390/s22145407 - 20 Jul 2022

Cited by 4 | Viewed by 2382

Abstract

Diagnosis of most neuropsychiatric disorders relies on subjective measures, which makes the reliability of final clinical decisions questionable. The aim of this study was to propose a machine learning-based classification approach for objective diagnosis of three disorders of neuropsychiatric or neurological origin with functional near-infrared spectroscopy (fNIRS) derived biomarkers. Thirteen healthy adolescents and sixty-seven patients who were clinically diagnosed with migraine, obsessive compulsive disorder, or schizophrenia performed a Stroop task, while prefrontal cortex hemodynamics were monitored with fNIRS. Hemodynamic and cognitive features were extracted for training three supervised learning algorithms (naïve bayes (NB), linear discriminant analysis (LDA), and support vector machines (SVM)). The performance of each algorithm in correctly predicting the class of each participant across the four classes was tested with ten runs of a ten-fold cross-validation procedure. All algorithms achieved four-class classification performances with accuracies above 81% and specificities above 94%. SVM had the highest performance in terms of accuracy (85.1 ± 1.77%), sensitivity (84 ± 1.7%), specificity (95 ± 0.5%), precision (86 ± 1.6%), and F1-score (85 ± 1.7%). fNIRS-derived features have no subjective report bias when used for automated classification purposes. The presented methodology might have significant potential for assisting in the objective diagnosis of neuropsychiatric disorders associated with frontal lobe dysfunction. Full article

(This article belongs to the Special Issue Signal Processing for Brain–Computer Interfaces)

► Show Figures

Figure 1

20 pages, 1158 KiB

Open AccessArticle

Unmanned Aerial Vehicle for Laser Based Biomedical Sensor Development and Examination of Device Trajectory

by Usman Masud, Tareq Saeed, Faraz Akram, Hunida Malaikah and Altaf Akbar

Sensors 2022, 22(9), 3413; https://0-doi-org.brum.beds.ac.uk/10.3390/s22093413 - 29 Apr 2022

Cited by 9 | Viewed by 1908

Abstract

Controller design and signal processing for the control of air-vehicles have gained extreme importance while interacting with humans to form a brain–computer interface. This is because fewer commands need to be mapped into multiple controls. For our anticipated biomedical sensor for breath analysis, it is mandatory to provide medication to the patients on an urgent basis. To address this increasingly tense situation in terms of emergencies, we plan to design an unmanned vehicle that can aid spontaneously to monitor the person’s health, and help the physician spontaneously during the rescue mission. Simultaneously, that must be done in such a computationally efficient algorithm that the minimum amount of energy resources are consumed. For this purpose, we resort to an unmanned logistic air-vehicle which flies from the medical centre to the affected person. After obtaining restricted permission from the regional administration, numerous challenges are identified for this design. The device is able to lift a weight of 2 kg successfully which is required for most emergency medications, while choosing the smallest distance to the destination with the GPS. By recording the movement of the vehicle in numerous directions, the results deviate to a maximum of 2% from theoretical investigations. In this way, our biomedical sensor provides critical information to the physician, who is able to provide medication to the patient urgently. On account of reasonable supply of medicines to the destination in terms of weight and time, this experimentation has been rendered satisfactory by the relevant physicians in the vicinity. Full article

(This article belongs to the Special Issue Signal Processing for Brain–Computer Interfaces)

► Show Figures

Figure 1

12 pages, 1588 KiB

Open AccessArticle

LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI

by Asma Gulraiz, Noman Naseer, Hammad Nazeer, Muhammad Jawad Khan, Rayyan Azam Khan and Umar Shahbaz Khan

Sensors 2022, 22(7), 2575; https://0-doi-org.brum.beds.ac.uk/10.3390/s22072575 - 28 Mar 2022

Cited by 8 | Viewed by 2440

Abstract

Brain-computer interface (BCI) systems based on functional near-infrared spectroscopy (fNIRS) have been used as a way of facilitating communication between the brain and peripheral devices. The BCI provides an option to improve the walking pattern of people with poor walking dysfunction, by applying a rehabilitation process. A state-of-the-art step-wise BCI system includes data acquisition, pre-processing, channel selection, feature extraction, and classification. In fNIRS-based BCI (fNIRS-BCI), channel selection plays a vital role in enhancing the classification accuracy of the BCI problem. In this study, the concentration of blood oxygenation (HbO) in a resting state and in a walking state was used to decode the walking activity and the resting state of the subject, using channel selection by Least Absolute Shrinkage and Selection Operator (LASSO) homotopy-based sparse representation classification. The fNIRS signals of nine subjects were collected from the left hemisphere of the primary motor cortex. The subjects performed the task of walking on a treadmill for 10 s, followed by a 20 s rest. Appropriate filters were applied to the collected signals to remove motion artifacts and physiological noises. LASSO homotopy-based sparse representation was used to select the most significant channels, and then classification was performed to identify walking and resting states. For comparison, the statistical spatial features of mean, peak, variance, and skewness, and their combination, were used for classification. The classification results after channel selection were then compared with the classification based on the extracted features. The classifiers used for both methods were linear discrimination analysis (LDA), support vector machine (SVM), and logistic regression (LR). The study found that LASSO homotopy-based sparse representation classification successfully discriminated between the walking and resting states, with a better average classification accuracy (p < 0.016) of 91.32%. This research provides a step forward in improving the classification accuracy of fNIRS-BCI systems. The proposed methodology may also be used for rehabilitation purposes, such as controlling wheelchairs and prostheses, as well as an active rehabilitation training technique for patients with motor dysfunction. Full article

(This article belongs to the Special Issue Signal Processing for Brain–Computer Interfaces)

► Show Figures

Figure 1

17 pages, 28674 KiB

Open AccessArticle

Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks

by Huma Hamid, Noman Naseer, Hammad Nazeer, Muhammad Jawad Khan, Rayyan Azam Khan and Umar Shahbaz Khan

Sensors 2022, 22(5), 1932; https://0-doi-org.brum.beds.ac.uk/10.3390/s22051932 - 01 Mar 2022

Cited by 20 | Viewed by 4681

Abstract

This research presents a brain-computer interface (BCI) framework for brain signal classification using deep learning (DL) and machine learning (ML) approaches on functional near-infrared spectroscopy (fNIRS) signals. fNIRS signals of motor execution for walking and rest tasks are acquired from the primary motor cortex in the brain’s left hemisphere for nine subjects. DL algorithms, including convolutional neural networks (CNNs), long short-term memory (LSTM), and bidirectional LSTM (Bi-LSTM) are used to achieve average classification accuracies of 88.50%, 84.24%, and 85.13%, respectively. For comparison purposes, three conventional ML algorithms, support vector machine (SVM), k-nearest neighbor (k-NN), and linear discriminant analysis (LDA) are also used for classification, resulting in average classification accuracies of 73.91%, 74.24%, and 65.85%, respectively. This study successfully demonstrates that the enhanced performance of fNIRS-BCI can be achieved in terms of classification accuracy using DL approaches compared to conventional ML approaches. Furthermore, the control commands generated by these classifiers can be used to initiate and stop the gait cycle of the lower limb exoskeleton for gait rehabilitation. Full article

(This article belongs to the Special Issue Signal Processing for Brain–Computer Interfaces)

► Show Figures

Figure 1

14 pages, 2160 KiB

Open AccessArticle

Single-Trial Classification of Error-Related Potentials in People with Motor Disabilities: A Study in Cerebral Palsy, Stroke, and Amputees

by Nayab Usama, Imran Khan Niazi, Kim Dremstrup and Mads Jochumsen

Sensors 2022, 22(4), 1676; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041676 - 21 Feb 2022

Cited by 3 | Viewed by 2599

Abstract

Brain-computer interface performance may be reduced over time, but adapting the classifier could reduce this problem. Error-related potentials (ErrPs) could label data for continuous adaptation. However, this has scarcely been investigated in populations with severe motor impairments. The aim of this study was to detect ErrPs from single-trial EEG in offline analysis in participants with cerebral palsy, an amputation, or stroke, and determine how much discriminative information different brain regions hold. Ten participants with cerebral palsy, eight with an amputation, and 25 with a stroke attempted to perform 300–400 wrist and ankle movements while a sham BCI provided feedback on their performance for eliciting ErrPs. Pre-processed EEG epochs were inputted in a multi-layer perceptron artificial neural network. Each brain region was used as input individually (Frontal, Central, Temporal Right, Temporal Left, Parietal, and Occipital), the combination of the Central region with each of the adjacent regions, and all regions combined. The Frontal and Central regions were most important, and adding additional regions only improved performance slightly. The average classification accuracies were 84 ± 4%, 87± 4%, and 85 ± 3% for cerebral palsy, amputation, and stroke participants. In conclusion, ErrPs can be detected in participants with motor impairments; this may have implications for developing adaptive BCIs or automatic error correction. Full article

(This article belongs to the Special Issue Signal Processing for Brain–Computer Interfaces)

► Show Figures

Figure 1

16 pages, 5150 KiB

Open AccessArticle

fNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees

by Neelum Yousaf Sattar, Zareena Kausar, Syed Ali Usama, Umer Farooq, Muhammad Faizan Shah, Shaheer Muhammad, Razaullah Khan and Mohamed Badran

Sensors 2022, 22(3), 726; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030726 - 18 Jan 2022

Cited by 5 | Viewed by 3252

Abstract

Prosthetic arms are designed to assist amputated individuals in the performance of the activities of daily life. Brain machine interfaces are currently employed to enhance the accuracy as well as number of control commands for upper limb prostheses. However, the motion prediction for prosthetic arms and the rehabilitation of amputees suffering from transhumeral amputations is limited. In this paper, functional near-infrared spectroscopy (fNIRS)-based approach for the recognition of human intention for six upper limb motions is proposed. The data were extracted from the study of fifteen healthy subjects and three transhumeral amputees for elbow extension, elbow flexion, wrist pronation, wrist supination, hand open, and hand close. The fNIRS signals were acquired from the motor cortex region of the brain by the commercial NIRSport device. The acquired data samples were filtered using finite impulse response (FIR) filter. Furthermore, signal mean, signal peak and minimum values were computed as feature set. An artificial neural network (ANN) was applied to these data samples. The results show the likelihood of classifying the six arm actions with an accuracy of 78%. The attained results have not yet been reported in any identical study. These achieved fNIRS results for intention detection are promising and suggest that they can be applied for the real-time control of the transhumeral prosthesis. Full article

(This article belongs to the Special Issue Signal Processing for Brain–Computer Interfaces)

► Show Figures

Figure 1

15 pages, 3331 KiB

Open AccessArticle

Classification of Individual Finger Movements from Right Hand Using fNIRS Signals

by Haroon Khan, Farzan M. Noori, Anis Yazidi, Md Zia Uddin, M. N. Afzal Khan and Peyman Mirtaheri

Sensors 2021, 21(23), 7943; https://0-doi-org.brum.beds.ac.uk/10.3390/s21237943 - 28 Nov 2021

Cited by 7 | Viewed by 3007

Abstract

Functional near-infrared spectroscopy (fNIRS) is a comparatively new noninvasive, portable, and easy-to-use brain imaging modality. However, complicated dexterous tasks such as individual finger-tapping, particularly using one hand, have been not investigated using fNIRS technology. Twenty-four healthy volunteers participated in the individual finger-tapping experiment. Data were acquired from the motor cortex using sixteen sources and sixteen detectors. In this preliminary study, we applied standard fNIRS data processing pipeline, i.e., optical densities conversation, signal processing, feature extraction, and classification algorithm implementation. Physiological and non-physiological noise is removed using 4th order band-pass Butter-worth and 3rd order Savitzky–Golay filters. Eight spatial statistical features were selected: signal-mean, peak, minimum, Skewness, Kurtosis, variance, median, and peak-to-peak form data of oxygenated haemoglobin changes. Sophisticated machine learning algorithms were applied, such as support vector machine (SVM), random forests (RF), decision trees (DT), AdaBoost, quadratic discriminant analysis (QDA), Artificial neural networks (ANN), k-nearest neighbors (kNN), and extreme gradient boosting (XGBoost). The average classification accuracies achieved were

0.75 \pm 0.04

0.75 \pm 0.05

, and

0.77 \pm 0.06

using k-nearest neighbors (kNN), Random forest (RF) and XGBoost, respectively. KNN, RF and XGBoost classifiers performed exceptionally well on such a high-class problem. The results need to be further investigated. In the future, a more in-depth analysis of the signal in both temporal and spatial domains will be conducted to investigate the underlying facts. The accuracies achieved are promising results and could open up a new research direction leading to enrichment of control commands generation for fNIRS-based brain-computer interface applications. Full article

(This article belongs to the Special Issue Signal Processing for Brain–Computer Interfaces)

► Show Figures

Figure 1

Review

Jump to: Editorial, Research

22 pages, 3222 KiB

Open AccessReview

Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review

by Rihui Li, Dalin Yang, Feng Fang, Keum-Shik Hong, Allan L. Reiss and Yingchun Zhang

Sensors 2022, 22(15), 5865; https://0-doi-org.brum.beds.ac.uk/10.3390/s22155865 - 05 Aug 2022

Cited by 34 | Viewed by 8451

Abstract

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) stand as state-of-the-art techniques for non-invasive functional neuroimaging. On a unimodal basis, EEG has poor spatial resolution while presenting high temporal resolution. In contrast, fNIRS offers better spatial resolution, though it is constrained by its poor temporal resolution. One important merit shared by the EEG and fNIRS is that both modalities have favorable portability and could be integrated into a compatible experimental setup, providing a compelling ground for the development of a multimodal fNIRS–EEG integration analysis approach. Despite a growing number of studies using concurrent fNIRS-EEG designs reported in recent years, the methodological reference of past studies remains unclear. To fill this knowledge gap, this review critically summarizes the status of analysis methods currently used in concurrent fNIRS–EEG studies, providing an up-to-date overview and guideline for future projects to conduct concurrent fNIRS–EEG studies. A literature search was conducted using PubMed and Web of Science through 31 August 2021. After screening and qualification assessment, 92 studies involving concurrent fNIRS–EEG data recordings and analyses were included in the final methodological review. Specifically, three methodological categories of concurrent fNIRS–EEG data analyses, including EEG-informed fNIRS analyses, fNIRS-informed EEG analyses, and parallel fNIRS–EEG analyses, were identified and explained with detailed description. Finally, we highlighted current challenges and potential directions in concurrent fNIRS–EEG data analyses in future research. Full article

(This article belongs to the Special Issue Signal Processing for Brain–Computer Interfaces)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Signal Processing for Brain–Computer Interfaces

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (12 papers)

Editorial

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI