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Novel Approaches to EEG Signal Processing

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

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 63478

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Prof. Dr. Francesco Carlo Morabito

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Department of Information Engineering, Infrastructures and Sustainable Energy (DIIES), University “Mediterranea” of Reggio Calabria, Via Graziella, Loc. Feo di Vito, 89124 Reggio di Calabria, Italy
Interests: computational intelligence; biomedical signal processing; data fusion; evaluation; complexity analysis; deep learning; brain complex networks; non-destructive testing & entropic methods

Dr. Yangsong Zhang

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School of Computer Science and Technology, Laboratory for Brain Science and Medical Artificial Intelligence, Southwest University of Science and Technology, Mianyang 621010, China
Interests: deep learning; brain-apparatus communication; machine learning; biomedical engineering; medical imaging processing; computational neuroscience; brain network analysis

Dr. Zhilin Zhang

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AT&T, Dallas, Fort Worth Area, Texas, USA
Interests: sparse signal recovery; machine learning; blind signal separation; wearable healthcare; biomedical signal processing; data mining for neurological disease study; brain-computer interfaces

Special Issue Information

Dear Colleagues,

Electroencephalography (EEG) is today a routinely well-established diagnostic technique that has extensively been applied to study brain functioning in health and disease. EEG signal processing involves the acquisition, analysis and treatment of the electrical activity of the brain measured using electroencephalography. The recent advances in signal processing and machine learning for EEG data processing have brought impressive progress in terms of solving several practical and challenging problems in many areas such as healthcare, biomedicine, biomedical engineering, BCI and biometrics.

The aim of this Special Issue is to present and discuss the most recent advances in EEG signal analysis and processing. We are inviting original research work covering novel theories, innovative methods, advanced technologies, fusion with other diagnostics and meaningful applications that can potentially lead to significant advances in EEG data analytics.

Prof. Dr. Francesco Carlo Morabito
Dr. Yangsong Zhang
Dr. Zhilin Zhang
Guest Editors

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Keywords

EEG Signal Processing
Brain–Computer Interface
Machine Learning
Deep Learning
High-Density EEG
Convolutional Neural Networks
EEG Autoencoders
Generative Adversarial Networks for EEG
Data Fusion Techniques for Brain Data
EEG Data Analytics
EEG Compressive Sensing
Brain States Classification from EEG
Advanced artefact reduction in EEG
EEG Feature Selection and Generation
Wearable EEG
Virtual Reality with EEG

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Published Papers (12 papers)

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22 pages, 5023 KiB

Open AccessArticle

A Computerized Bioinspired Methodology for Lightweight and Reliable Neural Telemetry

by Olufemi Adeluyi, Miguel A. Risco-Castillo, María Liz Crespo, Andres Cicuttin and Jeong-A Lee

Sensors 2020, 20(22), 6461; https://doi.org/10.3390/s20226461 - 12 Nov 2020

Viewed by 2377

Abstract

Personalized health monitoring of neural signals usually results in a very large dataset, the processing and transmission of which require considerable energy, storage, and processing time. We present bioinspired electroceptive compressive sensing (BeCoS) as an approach for minimizing these penalties. It is a lightweight and reliable approach for the compression and transmission of neural signals inspired by active electroceptive sensing used by weakly electric fish. It uses a signature signal and a sensed pseudo-sparse differential signal to transmit and reconstruct the signals remotely. We have used EEG datasets to compare BeCoS with the block sparse Bayesian learning-bound optimization (BSBL-BO) technique—A popular compressive sensing technique used for low-energy wireless telemonitoring of EEG signals. We achieved average coherence, latency, compression ratio, and estimated per-epoch power values that were 35.38%, 62.85%, 53.26%, and 13 mW better than BSBL-BO, respectively, while structural similarity was only 6.295% worse. However, the original and reconstructed signals remain visually similar. BeCoS senses the signals as a derivative of a predefined signature signal resulting in a pseudo-sparse signal that significantly improves the efficiency of the monitoring process. The results show that BeCoS is a promising approach for the health monitoring of neural signals. Full article

(This article belongs to the Special Issue Novel Approaches to EEG Signal Processing)

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20 pages, 3827 KiB

Open AccessArticle

Deep Neural Network for Visual Stimulus-Based Reaction Time Estimation Using the Periodogram of Single-Trial EEG

by Mohammad Samin Nur Chowdhury, Arindam Dutta, Matthew Kyle Robison, Chris Blais, Gene Arnold Brewer and Daniel Wesley Bliss

Sensors 2020, 20(21), 6090; https://doi.org/10.3390/s20216090 - 27 Oct 2020

Cited by 9 | Viewed by 5145

Abstract

Multiplexed deep neural networks (DNN) have engendered high-performance predictive models gaining popularity for decoding brain waves, extensively collected in the form of electroencephalogram (EEG) signals. In this paper, to the best of our knowledge, we introduce a first-ever DNN-based generalized approach to estimate reaction time (RT) using the periodogram representation of single-trial EEG in a visual stimulus-response experiment with 48 participants. We have designed a Fully Connected Neural Network (FCNN) and a Convolutional Neural Network (CNN) to predict and classify RTs for each trial. Though deep neural networks are widely known for classification applications, cascading FCNN/CNN with the Random Forest model, we designed a robust regression-based estimator to predict RT. With the FCNN model, the accuracies obtained for binary and 3-class classification were 93% and 76%, respectively, which further improved with the use of CNN (94% and 78%, respectively). The regression-based approach predicted RTs with correlation coefficients (CC) of 0.78 and 0.80 for FCNN and CNN, respectively. Investigating further, we found that the left central as well as parietal and occipital lobes were crucial for predicting RT, with significant activities in the theta and alpha frequency bands. Full article

(This article belongs to the Special Issue Novel Approaches to EEG Signal Processing)

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21 pages, 4282 KiB

Open AccessArticle

Exploring the Limitations of Event-Related Potential Measures in Moving Subjects: Pilot Studies of Four Different Technical Modifications in Ergometer Rowing

by Holger Hill

Sensors 2020, 20(19), 5618; https://doi.org/10.3390/s20195618 - 1 Oct 2020

Cited by 2 | Viewed by 2243

Abstract

Measuring brain activity in moving subjects is of great importance for investigating human behavior in ecological settings. For this purpose, EEG measures are applicable; however, technical modifications are required to reduce the typical massive movement artefacts. Four different approaches to measure EEG/ERPs during rowing were tested: (i) a purpose-built head-mounted preamplifier, (ii) a laboratory system with active electrodes, and a wireless headset combined with (iii) passive or (iv) active electrodes. A standard visual oddball task revealed very similar (within subjects) visual evoked potentials for rowing and rest (without movement). The small intraindividual differences between rowing and rest, in comparison to the typically larger interindividual differences in the ERP waveforms, revealed that ERPs can be measured reliably even in an athletic movement such as rowing. On the other hand, the expected modulation of the motor-related activity by force output was largely affected by movement artefacts. Therefore, for a successful application of ERP measures in movement research, further developments to differentiate between movement-related neuronal activity and movement-related artefacts are required. However, activities with small magnitudes related to motor learning and motor control may be difficult to detect because they are superimposed by the very large motor potential, which increases with force output. Full article

(This article belongs to the Special Issue Novel Approaches to EEG Signal Processing)

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16 pages, 2575 KiB

Open AccessArticle

Latent Phase Identification of High-Frequency Micro-Scale Gamma Spike Transients in the Hypoxic Ischemic EEG of Preterm Fetal Sheep Using Spectral Analysis and Fuzzy Classifiers

by Hamid Abbasi, Alistair J. Gunn, Laura Bennet and Charles P. Unsworth

Sensors 2020, 20(5), 1424; https://doi.org/10.3390/s20051424 - 5 Mar 2020

Cited by 9 | Viewed by 3390

Abstract

Premature babies are at high risk of serious neurodevelopmental disabilities, which in many cases are related to perinatal hypoxic–ischemic encephalopathy (HIE). Studies of neuroprotection in animal models consistently suggest that treatment must be started as early as possible in the first 6 h after hypoxia–ischemia (HI), the so-called latent phase before secondary deterioration, to improve outcomes. We have shown in preterm sheep that EEG biomarkers of injury, in the form of high-frequency micro-scale spike transients, develop and evolve in this critical latent phase after severe asphyxia. Real-time automatic identification of such events is important for the early and accurate detection of HI injury, so that the right treatment can be implemented at the right time. We have previously reported successful strategies for accurate identification of EEG patterns after HI. In this study, we report an alternative high-performance approach based on the fusion of spectral Fourier analysis and Type-I fuzzy classifiers (FFT-Type-I-FLC). We assessed its performance in over 2520 min of latent phase EEG recordings from seven asphyxiated in utero preterm fetal sheep exposed to a range of different occlusion periods. The FFT-Type-I-FLC classifier demonstrated 98.9 ± 1.0% accuracy for identification of high-frequency spike transients in the gamma frequency band (namely 80–120 Hz) post-HI. The spectral-based approach (FFT-Type-I-FLC classifier) has similar accuracy to our previous reverse biorthogonal wavelets rbio2.8 basis function and type-1 fuzzy classifier (rbio-WT-Type-1-FLC), providing competitive performance (within the margin of error: 0.89%), but it is computationally simpler and would be readily adapted to identify other potentially relevant EEG waveforms. Full article

(This article belongs to the Special Issue Novel Approaches to EEG Signal Processing)

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12 pages, 744 KiB

Open AccessArticle

A Comprehensive Machine-Learning-Based Software Pipeline to Classify EEG Signals: A Case Study on PNES vs. Control Subjects

by Giuseppe Varone, Sara Gasparini, Edoardo Ferlazzo, Michele Ascoli, Giovanbattista Gaspare Tripodi, Chiara Zucco, Barbara Calabrese, Mario Cannataro and Umberto Aguglia

Sensors 2020, 20(4), 1235; https://doi.org/10.3390/s20041235 - 24 Feb 2020

Cited by 17 | Viewed by 4738

Abstract

The diagnosis of psychogenic nonepileptic seizures (PNES) by means of electroencephalography (EEG) is not a trivial task during clinical practice for neurologists. No clear PNES electrophysiological biomarker has yet been found, and the only tool available for diagnosis is video EEG monitoring with recording of a typical episode and clinical history of the subject. In this paper, a data-driven machine learning (ML) pipeline for classifying EEG segments (i.e., epochs) of PNES and healthy controls (CNT) is introduced. This software pipeline consists of a semiautomatic signal processing technique and a supervised ML classifier to aid clinical discriminative diagnosis of PNES by means of an EEG time series. In our ML pipeline, statistical features like the mean, standard deviation, kurtosis, and skewness are extracted in a power spectral density (PSD) map split up in five conventional EEG rhythms (delta, theta, alpha, beta, and the whole band, i.e., 1–32 Hz). Then, the feature vector is fed into three different supervised ML algorithms, namely, the support vector machine (SVM), linear discriminant analysis (LDA), and Bayesian network (BN), to perform EEG segment classification tasks for CNT vs. PNES. The performance of the pipeline algorithm was evaluated on a dataset of 20 EEG signals (10 PNES and 10 CNT) that was recorded in eyes-closed resting condition at the Regional Epilepsy Centre, Great Metropolitan Hospital of Reggio Calabria, University of Catanzaro, Italy. The experimental results showed that PNES vs. CNT discrimination tasks performed via the ML algorithm and validated with random split (RS) achieved an average accuracy of 0.97 ± 0.013 (RS-SVM), 0.99 ± 0.02 (RS-LDA), and 0.82 ± 0.109 (RS-BN). Meanwhile, with leave-one-out (LOO) validation, an average accuracy of 0.98 ± 0.0233 (LOO-SVM), 0.98 ± 0.124 (LOO-LDA), and 0.81 ± 0.109 (LOO-BN) was achieved. Our findings showed that BN was outperformed by SVM and LDA. The promising results of the proposed software pipeline suggest that it may be a valuable tool to support existing clinical diagnosis. Full article

(This article belongs to the Special Issue Novel Approaches to EEG Signal Processing)

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12 pages, 2390 KiB

Open AccessArticle

Prediction of Individual User’s Dynamic Ranges of EEG Features from Resting-State EEG Data for Evaluating Their Suitability for Passive Brain–Computer Interface Applications

by Ho-Seung Cha, Chang-Hee Han and Chang-Hwan Im

Sensors 2020, 20(4), 988; https://doi.org/10.3390/s20040988 - 12 Feb 2020

Cited by 5 | Viewed by 3408

Abstract

With the recent development of low-cost wearable electroencephalogram (EEG) recording systems, passive brain–computer interface (pBCI) applications are being actively studied for a variety of application areas, such as education, entertainment, and healthcare. Various EEG features have been employed for the implementation of pBCI applications; however, it is frequently reported that some individuals have difficulty fully enjoying the pBCI applications because the dynamic ranges of their EEG features (i.e., its amplitude variability over time) were too small to be used in the practical applications. Conducting preliminary experiments to search for the individualized EEG features associated with different mental states can partly circumvent this issue; however, these time-consuming experiments were not necessary for the majority of users whose dynamic ranges of EEG features are large enough to be used for pBCI applications. In this study, we tried to predict an individual user’s dynamic ranges of the EEG features that are most widely employed for pBCI applications from resting-state EEG (RS-EEG), with the ultimate goal of identifying individuals who might need additional calibration to become suitable for the pBCI applications. We employed a machine learning-based regression model to predict the dynamic ranges of three widely used EEG features known to be associated with the brain states of valence, relaxation, and concentration. Our results showed that the dynamic ranges of EEG features could be predicted with normalized root mean squared errors of 0.2323, 0.1820, and 0.1562, respectively, demonstrating the possibility of predicting the dynamic ranges of the EEG features for pBCI applications using short resting EEG data. Full article

(This article belongs to the Special Issue Novel Approaches to EEG Signal Processing)

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21 pages, 4782 KiB

Open AccessArticle

Super-Resolution for Improving EEG Spatial Resolution using Deep Convolutional Neural Network—Feasibility Study

by Moonyoung Kwon, Sangjun Han, Kiwoong Kim and Sung Chan Jun

Sensors 2019, 19(23), 5317; https://doi.org/10.3390/s19235317 - 3 Dec 2019

Cited by 25 | Viewed by 5806

Abstract

Electroencephalography (EEG) has relatively poor spatial resolution and may yield incorrect brain dynamics and distort topography; thus, high-density EEG systems are necessary for better analysis. Conventional methods have been proposed to solve these problems, however, they depend on parameters or brain models that are not simple to address. Therefore, new approaches are necessary to enhance EEG spatial resolution while maintaining its data properties. In this work, we investigated the super-resolution (SR) technique using deep convolutional neural networks (CNN) with simulated EEG data with white Gaussian and real brain noises, and experimental EEG data obtained during an auditory evoked potential task. SR EEG simulated data with white Gaussian noise or brain noise demonstrated a lower mean squared error and higher correlations with sensor information, and detected sources even more clearly than did low resolution (LR) EEG. In addition, experimental SR data also demonstrated far smaller errors for N1 and P2 components, and yielded reasonable localized sources, while LR data did not. We verified our proposed approach’s feasibility and efficacy, and conclude that it may be possible to explore various brain dynamics even with a small number of sensors. Full article

(This article belongs to the Special Issue Novel Approaches to EEG Signal Processing)

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18 pages, 2867 KiB

Open AccessArticle

Neural Activities Classification of Human Inhibitory Control Using Hierarchical Model

by Rupesh Kumar Chikara and Li-Wei Ko

Sensors 2019, 19(17), 3791; https://doi.org/10.3390/s19173791 - 1 Sep 2019

Cited by 26 | Viewed by 5132

Abstract

Human inhibitory control refers to the suppression of behavioral response in real environments, such as when driving a car or riding a motorcycle, playing a game and operating a machine. The P300 wave is a neural marker of human inhibitory control, and it can be used to recognize the symptoms of attention deficit hyperactivity disorder (ADHD) in human. In addition, the P300 neural marker can be considered as a stop command in the brain-computer interface (BCI) technologies. Therefore, the present study of electroencephalography (EEG) recognizes the mindset of human inhibition by observing the brain dynamics, like P300 wave in the frontal lobe, supplementary motor area, and in the right temporoparietal junction of the brain, all of them have been associated with response inhibition. Our work developed a hierarchical classification model to identify the neural activities of human inhibition. To accomplish this goal phase-locking value (PLV) method was used to select coupled brain regions related to inhibition because this method has demonstrated the best performance of the classification system. The PLVs were used with pattern recognition algorithms to classify a successful-stop versus a failed-stop in left-and right-hand inhibitions. The results demonstrate that quadratic discriminant analysis (QDA) yielded an average classification accuracy of 94.44%. These findings implicate the neural activities of human inhibition can be utilized as a stop command in BCI technologies, as well as to identify the symptoms of ADHD patients in clinical research. Full article

(This article belongs to the Special Issue Novel Approaches to EEG Signal Processing)

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22 pages, 7839 KiB

Open AccessArticle

An Unsupervised Method for Artefact Removal in EEG Signals

by Angel Mur, Raquel Dormido and Natividad Duro

Sensors 2019, 19(10), 2302; https://doi.org/10.3390/s19102302 - 18 May 2019

Cited by 11 | Viewed by 7547

Abstract

Objective: The activity of the brain can be recorded by means of an electroencephalogram (EEG). An EEG is a multichannel signal related to brain activity. However, EEG presents a wide variety of undesired artefacts. Removal of these artefacts is often done using blind source separation methods (BSS) and mainly those based on Independent Component Analysis (ICA). ICA-based methods are well-accepted in the literature for filtering artefacts and have proved to be satisfactory in most scenarios of interest. Our goal is to develop a generic and unsupervised ICA-based algorithm for EEG artefacts removal. Approach: The proposed algorithm makes use of a new unsupervised artefact detection, ICA and a statistical criterion to automatically select the artefact related independent components (ICs) requiring no human intervention. The algorithm is evaluated using both simulated and real EEG data with artefacts (SEEG and AEEG). A comparison between the proposed unsupervised selection of ICs related to the artefact and other supervised selection is also presented. Main results: A new unsupervised ICA-based algorithm to filter artefacts, where ICs related to each artefact are automatically selected. It can be used in online applications, it preserves most of the original information among the artefacts and removes different types of artefacts. Significance: ICA-based methods for filtering artefacts prevail in the literature. The work in this article is important insofar as it addresses the problem of automatic selection of ICs in ICA-based methods. The selection is unsupervised, avoiding the manual ICs selection or a learning process involved in other methods. Our method is a generic algorithm that allows removing EEG artefacts of various types and, unlike some ICA-based algorithms, it retains most of the original information among the artefacts. Within the algorithm, the artefact detection method implemented does not require human intervention either. Full article

(This article belongs to the Special Issue Novel Approaches to EEG Signal Processing)

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16 pages, 5852 KiB

Open AccessArticle

Emotion Recognition from Multiband EEG Signals Using CapsNet

by Hao Chao, Liang Dong, Yongli Liu and Baoyun Lu

Sensors 2019, 19(9), 2212; https://doi.org/10.3390/s19092212 - 13 May 2019

Cited by 237 | Viewed by 11512

Abstract

Emotion recognition based on multi-channel electroencephalograph (EEG) signals is becoming increasingly attractive. However, the conventional methods ignore the spatial characteristics of EEG signals, which also contain salient information related to emotion states. In this paper, a deep learning framework based on a multiband feature matrix (MFM) and a capsule network (CapsNet) is proposed. In the framework, the frequency domain, spatial characteristics, and frequency band characteristics of the multi-channel EEG signals are combined to construct the MFM. Then, the CapsNet model is introduced to recognize emotion states according to the input MFM. Experiments conducted on the dataset for emotion analysis using EEG, physiological, and video signals (DEAP) indicate that the proposed method outperforms most of the common models. The experimental results demonstrate that the three characteristics contained in the MFM were complementary and the capsule network was more suitable for mining and utilizing the three correlation characteristics. Full article

(This article belongs to the Special Issue Novel Approaches to EEG Signal Processing)

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12 pages, 4266 KiB

Open AccessLetter

Deep-Asymmetry: Asymmetry Matrix Image for Deep Learning Method in Pre-Screening Depression

by Min Kang, Hyunjin Kwon, Jin-Hyeok Park, Seokhwan Kang and Youngho Lee

Sensors 2020, 20(22), 6526; https://doi.org/10.3390/s20226526 - 15 Nov 2020

Cited by 38 | Viewed by 5266

Abstract

To have an objective depression diagnosis, numerous studies based on machine learning and deep learning using electroencephalogram (EEG) have been conducted. Most studies depend on one-dimensional raw data and required fine feature extraction. To solve this problem, in the EEG visualization research field, short-time Fourier transform (STFT), wavelet, and coherence commonly used as method s for transferring EEG data to 2D images. However, we devised a new way from the concept that EEG’s asymmetry was considered one of the major biomarkers of depression. This study proposes a deep-asymmetry methodology that converts the EEG’s asymmetry feature into a matrix image and uses it as input to a convolutional neural network. The asymmetry matrix image in the alpha band achieved 98.85% accuracy and outperformed most of the methods presented in previous studies. This study indicates that the proposed method can be an effective tool for pre-screening major depressive disorder patients. Full article

(This article belongs to the Special Issue Novel Approaches to EEG Signal Processing)

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14 pages, 677 KiB

Open AccessLetter

by Alexey N. Pavlov, Elena N. Pitsik, Nikita S. Frolov, Artem Badarin, Olga N. Pavlova and Alexander E. Hramov

Sensors 2020, 20(20), 5843; https://doi.org/10.3390/s20205843 - 15 Oct 2020

Cited by 23 | Viewed by 2611

Abstract

The problem of revealing age-related distinctions in multichannel electroencephalograms (EEGs) during the execution of motor tasks in young and elderly adults is addressed herein. Based on the detrended fluctuation analysis (DFA), differences in long-range correlations are considered, emphasizing changes in the scaling exponent [...] Read more.

α

. Stronger responses in elderly subjects are confirmed, including the range and rate of increase in

α

. Unlike elderly subjects, young adults demonstrated about 2.5 times more pronounced differences between motor task responses with the dominant and non-dominant hand. Knowledge of age-related changes in brain electrical activity is important for understanding consequences of healthy aging and distinguishing them from pathological changes associated with brain diseases. Besides diagnosing age-related effects, the potential of DFA can also be used in the field of brain–computer interfaces. Full article

(This article belongs to the Special Issue Novel Approaches to EEG Signal Processing)

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