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Neurophysiological Data Denoising and Enhancement

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A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (15 December 2018) | Viewed by 44491

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Special Issue Editors

Prof. Z. Jane Wang

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Guest Editor

Electrical and Computer Engineering Department, University of British Columbia, Vancouver, BC, Canada
Interests: statistical signal processing; machine learning; biomedical signal processing; medical imaging; medical data analytics; fMRI/EEG/EMG data processing

Prof. Dr. Xun Chen

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Guest Editor

Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China
Interests: artificial intelligence in medicine; human-machine interaction; multimodal image analysis; mobile health monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In current research and clinical communities, a variety of sensors for measuring and imaging neurophysiological activity exist, including electroencephalography (EEG), electromyography (EMG), magnetoencephalography (MEG), positron emission tomography (PET), functional magnetic resonance imaging (fMRI), functional near infrared (fNIR), diffusion tensor imaging (DTI), computed tomography (CT), and so on. Such sensor data (signals and images) are critically important for early detection, diagnosis, therapy, knowledge understanding and discovery in both clinical and pre-clinical scenarios. However, such collected neurophysiological data are inevitably corrupted by various degradations, inherent noise and artifacts. There are a number of critical issues regarding the enhancement of such neurophysiological signals and images captured by different sensing systems, such as motion-induced distortion and simultaneously measured uninterested information. Such effects are often neglected in conventional biomedical applications and have not been effectively solved in most cases. Therefore, efficient signal and image denoising and enhancement methods are required to ensure good data quality for further meaningful neurophysiological data analysis.

This Special Issue is, therefore, dedicated to the subject of neurophysiological signal and image enhancement techniques, mainly in suppression of artifact, noise, and interference of various forms. For this purpose, we invite researchers to contribute original research papers dedicated to developing advanced signal processing, data modeling and machine learning methods for promoting the robustness of various neurophysiological sensing systems.

Prof. Z. Jane Wang
Prof. Xun Chen
Guest Editors

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Keywords

Neurophysiological signal and image (e.g., EEG/EMG/ECG/MEG/fMRI) denoising and artifact removal;
Neuro-image (e.g., MRI/PET/CT/NIRS) enhancement: e.g., filtering, sharpening, contrast enhancement, histogram equalization, image up-sampling and super-resolution, etc. Different image segmentation, registration, and visualization and simulation techniques can be incorporated for enhancement;
Neurophysiological data quality assessment;
Advanced methods and models for the above topics: Examples include adaptive signal processing, blind source separation, deep learning (e.g., deep neural networks), tensor decomposition, transfer Learning, and other emerging significant methodologies;
Studies and applications of the above: Examples include brain-computer interfaces, bio-feedback and rehabilitation engineering, brain connectivity, biometrics, image registration, image fusion, and image inpainting and synthesis.

Published Papers (6 papers)

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Research

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12 pages, 1929 KiB

Open AccessArticle

Odor Recognition with a Spiking Neural Network for Bioelectronic Nose

by Ming Li, Haibo Ruan, Yu Qi, Tiantian Guo, Ping Wang and Gang Pan

Sensors 2019, 19(5), 993; https://doi.org/10.3390/s19050993 - 26 Feb 2019

Cited by 10 | Viewed by 5973

Abstract

Electronic noses recognize odors using sensor arrays, and usually face difficulties for odor complicacy, while animals have their own biological sensory capabilities for various types of odors. By implanting electrodes into the olfactory bulb of mammalian animals, odors may be recognized by decoding the recorded neural signals, in order to construct a bioelectronic nose. This paper proposes a spiking neural network (SNN)-based odor recognition method from spike trains recorded by the implanted electrode array. The proposed SNN-based approach exploits rich timing information well in precise time points of spikes. To alleviate the overfitting problem, we design a new SNN learning method with a voltage-based regulation strategy. Experiments are carried out using spike train signals recorded from the main olfactory bulb in rats. Results show that our SNN-based approach achieves the state-of-the-art performance, compared with other methods. With the proposed voltage regulation strategy, it achieves about 15% improvement compared with a classical SNN model. Full article

(This article belongs to the Special Issue Neurophysiological Data Denoising and Enhancement)

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14 pages, 3395 KiB

Open AccessArticle

Enhanced Auditory Steady-State Response Using an Optimized Chirp Stimulus-Evoked Paradigm

by Xiaoya Liu, Shuang Liu, Dongyue Guo, Yue Sheng, Yufeng Ke, Xingwei An, Feng He and Dong Ming

Sensors 2019, 19(3), 748; https://doi.org/10.3390/s19030748 - 12 Feb 2019

Cited by 9 | Viewed by 4459

Abstract

Objectives: It has been reported recently that gamma measures of the electroencephalogram (EEG) might provide information about the candidate biomarker of mental diseases like schizophrenia, Alzheimer’s disease, affective disorder and so on, but as we know it is a difficult issue to induce visual and tactile evoked responses at high frequencies. Although a high-frequency response evoked by auditory senses is achievable, the quality of the recording response is not ideal, such as relatively low signal-to-noise ratio (SNR). Recently, auditory steady-state responses (ASSRs) play an essential role in the field of basic auditory studies and clinical uses. However, how to improve the quality of ASSRs is still a challenge which researchers have been working on. This study aims at designing a more comfortable and suitable evoked paradigm and then enhancing the quality of the ASSRs in healthy subjects so as to further apply it in clinical practice. Methods: Chirp and click stimuli with 40 Hz and 60 Hz were employed to evoke the gamma-ASSR respectively, and the sound adjusted to 45 dB sound pressure level (SPL). Twenty healthy subjects with normal-hearing participated, and 64-channel EEGs were simultaneously recorded during the experiment. Event-related spectral perturbation (ERSP) and SNR of the ASSRs were measured and analyzed to verify the feasibility and adaptability of the proposed evoked paradigm. Results: The results showed that the evoked paradigm proposed in this study could enhance ASSRs with strong feasibility and adaptability. (1) ASSR waves in time domain indicated that 40 Hz stimuli could significantly induce larger peak-to-peak values of ASSRs compared to 60 Hz stimuli (p < 0.01**); ERSP showed that obvious ASSRs were obtained at each lead for both 40 Hz and 60 Hz, as well as the click and chirp stimuli. (2) The SNR of the ASSRs were –3.23 ± 1.68, –2.44 ± 2.90, –4.66 ± 2.09, and –3.53 ± 3.49 respectively for 40 Hz click, 40 Hz chirp, 60 Hz click and 60 Hz chirp, indicating the chirp stimuli could induce significantly better ASSR than the click, and 40 Hz ASSRs had the higher SNR than 60 Hz (p < 0.01**). Limitation: In this study, sample size was small and the age span was not large enough. Conclusions: This study verified the feasibility and adaptability of the proposed evoked paradigm to improve the quality of the gamma-ASSR, which is significant in clinical application. The results suggested that 40 Hz ASSR evoked by chirp stimuli had the best performance and was expected to be used in clinical practice, especially in the field of mental diseases such as schizophrenia, Alzheimer’s disease, and affective disorder. Full article

(This article belongs to the Special Issue Neurophysiological Data Denoising and Enhancement)

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13 pages, 3267 KiB

Open AccessArticle

Synergistic Myoelectrical Activities of Forearm Muscles Improving Robust Recognition of Multi-Fingered Gestures

by Xiuying Luo, Xiaoying Wu, Lin Chen, Yun Zhao, Li Zhang, Guanglin Li and Wensheng Hou

Sensors 2019, 19(3), 610; https://doi.org/10.3390/s19030610 - 01 Feb 2019

Cited by 24 | Viewed by 4840

Abstract

Currently, surface electromyography (sEMG) features of the forearm multi-tendon muscles are widely used in gesture recognition, however, there are few investigations on the inherent physiological mechanism of muscle synergies. We aimed to study whether the muscle synergies could be used for gesture recognition. Five healthy participants executed five gestures of daily life (pinch, fist, open hand, grip, and extension) and the sEMG activity was acquired from six forearm muscles. A non-negative matrix factorization (NMF) algorithm was employed to decompose the pre-treated six-channel sEMG data to obtain the muscle synergy matrixes, in which the weights of each muscle channel determined the feature set for hand gesture classification. The results showed that the synergistic features of forearm muscles could be successfully clustered in the feature space, which enabled hand gestures to be recognized with high efficiency. By augmenting the number of participants, the mean recognition rate remained at more than 96% and reflected high robustness. We showed that muscle synergies can be well applied to gesture recognition. Full article

(This article belongs to the Special Issue Neurophysiological Data Denoising and Enhancement)

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15 pages, 5118 KiB

Open AccessArticle

An Unsupervised Multichannel Artifact Detection Method for Sleep EEG Based on Riemannian Geometry

by Elizaveta Saifutdinova, Marco Congedo, Daniela Dudysova, Lenka Lhotska, Jana Koprivova and Vaclav Gerla

Sensors 2019, 19(3), 602; https://doi.org/10.3390/s19030602 - 31 Jan 2019

Cited by 10 | Viewed by 4021

Abstract

In biomedical signal processing, we often face the problem of artifacts that distort the original signals. This concerns also sleep recordings, such as EEG. Artifacts may severely affect or even make impossible visual inspection, as well as automatic processing. Many proposed methods concentrate on certain artifact types. Therefore, artifact-free data are often obtained after sequential application of different methods. Moreover, single-channel approaches must be applied to all channels alternately. The aim of this study is to develop a multichannel artifact detection method for multichannel sleep EEG capable of rejecting different artifact types at once. The inspiration for the study is gained from recent advances in the field of Riemannian geometry. The method we propose is tested on real datasets. The performance of the proposed method is measured by comparing detection results with the expert labeling as a reference and evaluated against a simpler method based on Riemannian geometry that has previously been proposed, as well as against the state-of-the-art method FASTER. The obtained results prove the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Neurophysiological Data Denoising and Enhancement)

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13 pages, 5849 KiB

Open AccessArticle

Spatial Reorganization of Myoelectric Activities in Extensor Digitorum for Sustained Finger Force Production

by Zhixian Gao, Shangjie Tang, Xiaoying Wu, Qiang Fu, Xingyu Fan, Yun Zhao, Lintao Hu, Lin Chen and Wensheng Hou

Sensors 2019, 19(3), 555; https://doi.org/10.3390/s19030555 - 29 Jan 2019

Cited by 2 | Viewed by 2738

Abstract

The study aims to explore the spatial distribution of multi-tendinous muscle modulated by central nervous system (CNS) during sustained contraction. Nine subjects were recruited to trace constant target forces with right index finger extension. Surface electromyography (sEMG) of extensor digitorum (ED) were recorded with a 32-channel electrode array. Nine successive topographic maps (TM) were obtained. Pixel wise analysis was utilized to extract subtracted topographic maps (STM), which exhibited inhomogeneous distribution. STMs were characterized into hot, warm, and cool regions corresponding to higher, moderate, and lower change ranges, respectively. The relative normalized area (normalized to the first phase) of these regions demonstrated different changing trends as rising, plateauing, and falling over time, respectively. Moreover, the duration of these trends were found to be affected by force level. The rising/falling periods were longer at lower force levels, while the plateau can be achieved from the initial phase for higher force output (45% maximal voluntary contraction). The results suggested muscle activity reorganization in ED plays a role to maintain sustained contraction. Furthermore, the decreased dynamical regulation ability to spatial reorganization may be prone to induce fatigue. This finding implied that spatial reorganization of muscle activity as a regulation mechanism contribute to maintain constant force production. Full article

(This article belongs to the Special Issue Neurophysiological Data Denoising and Enhancement)

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Review

Jump to: Research

18 pages, 2147 KiB

Open AccessReview

Removal of Artifacts from EEG Signals: A Review

by Xiao Jiang, Gui-Bin Bian and Zean Tian

Sensors 2019, 19(5), 987; https://doi.org/10.3390/s19050987 - 26 Feb 2019

Cited by 427 | Viewed by 21555

Abstract

Electroencephalogram (EEG) plays an important role in identifying brain activity and behavior. However, the recorded electrical activity always be contaminated with artifacts and then affect the analysis of EEG signal. Hence, it is essential to develop methods to effectively detect and extract the clean EEG data during encephalogram recordings. Several methods have been proposed to remove artifacts, but the research on artifact removal continues to be an open problem. This paper tends to review the current artifact removal of various contaminations. We first discuss the characteristics of EEG data and the types of different artifacts. Then, a general overview of the state-of-the-art methods and their detail analysis are presented. Lastly, a comparative analysis is provided for choosing a suitable methods according to particular application. Full article

(This article belongs to the Special Issue Neurophysiological Data Denoising and Enhancement)

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