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Application of Machine Learning in Electroencephalogram and Bio-Electricity Signal Processing

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 30206

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Dr. Hirokazu Doi

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Department of Information and Management Systems Engineering, Nagaoka University of Technology, Niigata 940-2188, Japan
Interests: social perception; emotion; attractiveness computing; EEG/ERP; digital phenotyping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Bioelectric signals contain a vast amount of information, and researchers in diverse fields including cognitive neuroscience, psychiatry, and affective engineering have utilized features extracted from bioelectric signals as a reliable and objective measure of human and animal physiological activation. The introduction of easy-to-use libraries of machine learning has made various kinds of machine learning algorithms accessible to researchers outside the engineering and data-science fields. Consequently, the application of machine learning has enabled researchers to gain novel insight into physiological functions and utilize bioelectric information that has hitherto been missed or neglected by traditional methods of signal processing.

This Special Issue on “Application of Machine Learning in Electroencephalogram and Bio-Electricity Signal Processing” aims to provide a platform to exchange information on the state of the art of bioelectric signal processing using machine learning techniques. Researchers are invited to submit original research articles and review articles relevant to this theme. Articles on application of machine learning in adjacent areas of research such as optical imaging of neural activation, e.g., near-infrared spectroscopy, and non-contact measurement of physiological responses are also welcome. Potential topics include but are not limited to the following:

Novel machine learning algorithm for bioelectricity data processing;
Application of machine learning in real-time processing of bioelectric signals;
Analysis of central and peripheral nervous system activation by machine learning;
Automatic classification of people with/without pathological conditions;
BCI/BMI.

Dr. Hirokazu Doi
Guest Editor

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Keywords

machine learning
EEG/ERP
ECG
BMI/BCI

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Published Papers (10 papers)

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Editorial

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4 pages, 173 KiB

Open AccessEditorial

Application of Machine Learning in Electroencephalogram and Bio-Electricity Signal Processing

by Hirokazu Doi

Appl. Sci. 2024, 14(23), 10980; https://doi.org/10.3390/app142310980 - 26 Nov 2024

Viewed by 751

Abstract

Since the seminal discovery of alpha waves by Hans Berger [...] Full article

(This article belongs to the Special Issue Application of Machine Learning in Electroencephalogram and Bio-Electricity Signal Processing)

Research

Jump to: Editorial

10 pages, 2784 KiB

Open AccessArticle

Recurrent Neural Network to Predict Saccade Offset Time Points from Electrooculogram Signals for Automatic Measurement of Eye-Fixation-Related Potential

by Takuma Saga, Hiroki Watanabe and Yasushi Naruse

Appl. Sci. 2023, 13(10), 6230; https://doi.org/10.3390/app13106230 - 19 May 2023

Cited by 1 | Viewed by 1546

Abstract

Eye-fixation-related potential (EFRP)—an event-related potential that is time-locked to the saccade offset (SO)—can be measured without synchronizing with time when external stimuli occur. Such an advantage in measurement enables the mean amplitude of the EFRP to be used to estimate the cognitive workload, which is known to change the amplitude, under real-world conditions. However, to observe EFRPs reliably, the SO timing must be correctly and consistently determined in milliseconds owing to the high temporal resolution of the electroencephalogram (EEG). As the electrooculogram (EOG) is commonly measured simultaneously with the EEG and the SO timing is reflected as a steep change in the waveforms, attempts have been made to determine the SO timing from EOG signals visually (the VD method). However, the SO timing detected by the VD method may be inconsistent across trials. We propose a gated recurrent unit—a recurrent neural network model—to detect the SO timing from EOGs consistently and automatically. We used EOG data from a task that mimics visual inspections, in which participants periodically traversed their eyes from left to right, for the model training. As a result, the amplitudes of the EFRPs based on the proposed method were significantly larger than those based on the VD method and the previous automatic method. This suggests that the proposed method can prevent the decrease in EFRP amplitudes owing to the inconsistent determination of the SO timing and increase the applicability of cognitive workload estimation using the EFRP in real-world environments. Full article

(This article belongs to the Special Issue Application of Machine Learning in Electroencephalogram and Bio-Electricity Signal Processing)

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17 pages, 2282 KiB

Open AccessArticle

Multivariate ERP Analysis of Neural Activations Underlying Processing of Aesthetically Manipulated Self-Face

by Hirokazu Doi

Appl. Sci. 2022, 12(24), 13007; https://doi.org/10.3390/app122413007 - 18 Dec 2022

Cited by 1 | Viewed by 2083

Abstract

Representation of self-face is vulnerable to cognitive bias, and consequently, people often possess a distorted image of self-face. The present study sought to investigate the neural mechanism underlying distortion of self-face representation by measuring event-related potentials (ERPs) elicited by actual, aesthetically enhanced, and degraded images of self-face. In addition to conventional analysis of ERP amplitude and global field power, multivariate analysis based on machine learning of single trial data were integrated into the ERP analysis. The multivariate analysis revealed differential pattern of scalp ERPs at a long latency range to self and other familiar faces when they were original or aesthetically degraded. The analyses of ERP amplitude and global field power failed to find any effects of experimental manipulation during long-latency range. The present results indicate the susceptibility of neural correlates of self-face representation to aesthetical manipulation and the usefulness of the machine learning approach in clarifying the neural mechanism underlying self-face processing. Full article

(This article belongs to the Special Issue Application of Machine Learning in Electroencephalogram and Bio-Electricity Signal Processing)

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15 pages, 2245 KiB

Open AccessArticle

Early Ventricular Fibrillation Prediction Based on Topological Data Analysis of ECG Signal

by Tianyi Ling, Ziyu Zhu, Yanbing Zhang and Fangfang Jiang

Appl. Sci. 2022, 12(20), 10370; https://doi.org/10.3390/app122010370 - 14 Oct 2022

Cited by 5 | Viewed by 3203

Abstract

Early ventricular fibrillation (VF) prediction is critical for prevention of sudden cardiac death, and can improve patient survival. Generally, electrocardiogram (ECG) signal features are extracted to predict VF, a process which plays an important role in prediction accuracy. Therefore, this study first proposes a novel feature based on topological data analysis (TDA) to improve the accuracy of early ventricular fibrillation prediction. Firstly, the heart activity is regarded as a cardiac dynamical system, which is described by phase space reconstruction. Then the topological structure of the phase space is characterized with persistent homology, and its statistical features are further extracted and defined as TDA features. Finally, 60 subjects (30 VF, 30 healthy) from three public ECG databases are used to validate the prediction performance of the proposed method. Compared to heart rate variability features and box-counting features, TDA features achieve a superior accuracy of 91.7%. Additionally, the three types of features are combined as fusion features, achieving the optimal accuracy of 95.0%. The fusion features are then ranked, and the first seven components are all from the TDA features. It follows that the proposed features provide a significant effect in improving the predictive performance of early VF. Full article

(This article belongs to the Special Issue Application of Machine Learning in Electroencephalogram and Bio-Electricity Signal Processing)

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17 pages, 3848 KiB

Open AccessArticle

sEMG Signals Characterization and Identification of Hand Movements by Machine Learning Considering Sex Differences

by Ruixuan Zhang, Xushu Zhang, Dongdong He, Ruixue Wang and Yuan Guo

Appl. Sci. 2022, 12(6), 2962; https://doi.org/10.3390/app12062962 - 14 Mar 2022

Cited by 12 | Viewed by 3532

Abstract

Developing a robust machine-learning algorithm to detect hand motion is one of the most challenging aspects of prosthetic hands and exoskeleton design. Machine-learning methods that considered sex differences were used to identify and describe hand movement patterns in healthy individuals. To this purpose, surface Electromyographic (sEMG) signals have been acquired from muscles in the forearm and hand. The results of statistical analysis indicated that most of the same muscle pairs in the right hand (females and males) showed significant differences during the six hand movements. Time features were used an as input to machine-learning algorithms for the recognition of six gestures. Specifically, two types of hand-gesture recognition methods that considered sex differences(differentiating sex datasets and adding a sex label)were proposed and applied to the k-nearest neighbor (k-NN), support vector machine (SVM) and artificial neural network (ANN) algorithms for comparison. In addition, a t-test statistical analysis approach and 5-fold cross validation were used as complements to verify whether considering sex differences could significantly improve classification performance. It was demonstrated that considering sex differences can significantly improve classification performance. The ANN algorithm with the addition of a sex label performed best in movement classification (98.4% accuracy). In the future, hand movement recognition algorithms considering sex differences could be applied to control systems for prosthetic hands or exoskeletons. Full article

(This article belongs to the Special Issue Application of Machine Learning in Electroencephalogram and Bio-Electricity Signal Processing)

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12 pages, 4246 KiB

Open AccessArticle

Blood Pressure Estimation by Photoplethysmogram Decomposition into Hyperbolic Secant Waves

by Takumi Nagasawa, Kaito Iuchi, Ryo Takahashi, Mari Tsunomura, Raquel Pantojo de Souza, Keiko Ogawa-Ochiai, Norimichi Tsumura and George C. Cardoso

Appl. Sci. 2022, 12(4), 1798; https://doi.org/10.3390/app12041798 - 9 Feb 2022

Cited by 14 | Viewed by 5276

Abstract

Photoplethysmographic (PPG) pulses contain information about cardiovascular parameters. In particular, blood pressure can be estimated using PPG pulse decomposition analysis, which assumes that a PPG pulse is composed of the original heart ejection blood wave and its reflections in arterial branchings. Among pulse decomposition wave functions that have been studied in the literature, Gaussian waves are the most successful ones. However, a more adequate pulse decomposition function could be found to improve blood pressure estimates. In this paper, we propose pulse decomposition analysis using hyperbolic secant (sech) waves and compare results with corresponding Gaussian wave decomposition. We analyze how the parameters of each of the two types of decomposition waves correlate with blood pressure. For this analysis, continuous blood pressure data and PPG data were acquired from ten healthy volunteers. The blood pressure of volunteers was varied by asking them to hold their breath for up to 60 s. The results suggested sech wave decomposition had higher accuracy in estimating blood pressure than the Gaussian function. Thus, sech wave decomposition should be considered as a more robust alternative to Gaussian wave pulse decomposition for blood pressure estimation models. Full article

(This article belongs to the Special Issue Application of Machine Learning in Electroencephalogram and Bio-Electricity Signal Processing)

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9 pages, 3308 KiB

Open AccessArticle

Machine Learning Based Color Classification by Means of Visually Evoked Potentials

by Carl Böck, Lea Meier, Stephan Kalb, Milan R. Vosko, Thomas Tschoellitsch, Mario Huemer and Jens Meier

Appl. Sci. 2021, 11(24), 11882; https://doi.org/10.3390/app112411882 - 14 Dec 2021

Cited by 4 | Viewed by 3290

Abstract

Visually evoked potentials (VEPs) are widely used for diagnoses of different neurological diseases. Interestingly, there is limited research about the impact of the stimulus color onto the evoked response. Therefore, in our study we investigated the possibility of automatically classifying the stimulus color. The visual stimuli were selected to be red/black and green/black checkerboard patterns with equal light density. Both of these stimuli were presented in a random manner to nine subjects, while the electroencephalogram was recorded at the occipital lobe. After pre-processing and aligning the evoked potentials, an artificial neural network with one hidden layer was used to investigate the general possibility to automatically classify the stimulus color in three different settings. First, color classification with individually trained models, color classification with a common model, and color classification for each individual volunteer with a model trained on the data of the remaining subjects. With an average accuracy (ACC) of 0.83, the best results were achieved for the individually trained model. Also, the second (mean ACC = 0.76) and third experiments (mean ACC = 0.71) indicated a reasonable predictive accuracy across all subjects. Consequently, machine learning tools are able to appropriately classify stimuli colors based on VEPs. Although further studies are needed to improve the classification performance of our approach, this opens new fields of applications for VEPs. Full article

(This article belongs to the Special Issue Application of Machine Learning in Electroencephalogram and Bio-Electricity Signal Processing)

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13 pages, 4663 KiB

Open AccessArticle

Creating a Diagnostic Assistance System for Diseases in Kampo Medicine

by Reimei Koike, Keiko Ogawa-Ochiai, Akiko Shirai, Katsumi Hayashi, Junsuke Arimitsu, Hongyang Li and Norimichi Tsumura

Appl. Sci. 2021, 11(21), 9716; https://doi.org/10.3390/app11219716 - 20 Oct 2021

Cited by 3 | Viewed by 2331

Abstract

The aim of this study was to propose a method to assess images of the tongue captured using a polarized light camera for diagnostic use in Kampo medicine. Glossy and non-glossy images of the tongue were captured simultaneously using a polarizing camera and a polarizing plate. Data augmentation was performed by modulating the color and gloss, resulting in an increase in the number of images from 11 to 275. To create a data set, the values for which diseases were evaluated by Kampo doctors for all tongue images were taken as the correct values and combined with the features extracted from the tongue images. Using this data set, we constructed a diagnostic support module to evaluate diseases. The resulting mean absolute error of the assessment was 0.44 for qi deficiency, 0.42 for blood deficiency, 0.33 for blood stagnation, 0.36 for yin deficiency, and 0.55 for fluid stagnation, suggesting that the diagnostic assistance module was accurate, and our proposed learning and data augmentation methods were effective. Full article

(This article belongs to the Special Issue Application of Machine Learning in Electroencephalogram and Bio-Electricity Signal Processing)

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11 pages, 422 KiB

Open AccessFeature PaperArticle

fNIRS-QC: Crowd-Sourced Creation of a Dataset and Machine Learning Model for fNIRS Quality Control

by Giulio Gabrieli, Andrea Bizzego, Michelle Jin Yee Neoh and Gianluca Esposito

Appl. Sci. 2021, 11(20), 9531; https://doi.org/10.3390/app11209531 - 14 Oct 2021

Cited by 8 | Viewed by 3950

Abstract

Despite technological advancements in functional Near Infra-Red Spectroscopy (fNIRS) and a rise in the application of the fNIRS in neuroscience experimental designs, the processing of fNIRS data remains characterized by a high number of heterogeneous approaches, implicating the scientific reproducibility and interpretability of the results. For example, a manual inspection is still necessary to assess the quality and subsequent retention of collected fNIRS signals for analysis. Machine Learning (ML) approaches are well-positioned to provide a unique contribution to fNIRS data processing by automating and standardizing methodological approaches for quality control, where ML models can produce objective and reproducible results. However, any successful ML application is grounded in a high-quality dataset of labeled training data, and unfortunately, no such dataset is currently available for fNIRS signals. In this work, we introduce fNIRS-QC, a platform designed for the crowd-sourced creation of a quality control fNIRS dataset. In particular, we (a) composed a dataset of 4385 fNIRS signals; (b) created a web interface to allow multiple users to manually label the signal quality of 510 10 s fNIRS segments. Finally, (c) a subset of the labeled dataset is used to develop a proof-of-concept ML model to automatically assess the quality of fNIRS signals. The developed ML models can serve as a more objective and efficient quality control check that minimizes error from manual inspection and the need for expertise with signal quality control. Full article

(This article belongs to the Special Issue Application of Machine Learning in Electroencephalogram and Bio-Electricity Signal Processing)

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17 pages, 1549 KiB

Open AccessArticle

An Ensemble Feature Selection Approach to Identify Relevant Features from EEG Signals

by Maritza Mera-Gaona, Diego M. López and Rubiel Vargas-Canas

Appl. Sci. 2021, 11(15), 6983; https://doi.org/10.3390/app11156983 - 29 Jul 2021

Cited by 12 | Viewed by 2692

Abstract

Identifying relevant data to support the automatic analysis of electroencephalograms (EEG) has become a challenge. Although there are many proposals to support the diagnosis of neurological pathologies, the current challenge is to improve the reliability of the tools to classify or detect abnormalities. In this study, we used an ensemble feature selection approach to integrate the advantages of several feature selection algorithms to improve the identification of the characteristics with high power of differentiation in the classification of normal and abnormal EEG signals. Discrimination was evaluated using several classifiers, i.e., decision tree, logistic regression, random forest, and Support Vecctor Machine (SVM); furthermore, performance was assessed by accuracy, specificity, and sensitivity metrics. The evaluation results showed that Ensemble Feature Selection (EFS) is a helpful tool to select relevant features from the EEGs. Thus, the stability calculated for the EFS method proposed was almost perfect in most of the cases evaluated. Moreover, the assessed classifiers evidenced that the models improved in performance when trained with the EFS approach’s features. In addition, the classifier of epileptiform events built using the features selected by the EFS method achieved an accuracy, sensitivity, and specificity of 97.64%, 96.78%, and 97.95%, respectively; finally, the stability of the EFS method evidenced a reliable subset of relevant features. Moreover, the accuracy, sensitivity, and specificity of the EEG detector are equal to or greater than the values reported in the literature. Full article

(This article belongs to the Special Issue Application of Machine Learning in Electroencephalogram and Bio-Electricity Signal Processing)

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