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Electroencephalogram Data Research Using Artificial Intelligence Technologies for Healthcare

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

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 32178

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Special Issue Editors

Dr. Tianning Li

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Guest Editor

School of Sciences, University of Southern Queensland, Toowoomba 4350, Australia
Interests: artificial intelligence; signal processing; EEG research

Prof. Dr. Xianpeng Wang

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Guest Editor

State Key Laboratory of Marine Resource Utilization in South China Sea, School of Information and Communication Engineering, Hainan University, Haikou 570228, China
Interests: array signal processing; wireless sensor network; MIMO radar
Special Issues, Collections and Topics in MDPI journals

Dr. Siuly Siuly

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Guest Editor

Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne 3011, Australia
Interests: biomedical signal processing; artificial intelligence (AI); data mining; detection of neurological diseases from EEGs; brain–computer interface (BCI)
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaohui Tao

E-Mail Website
Guest Editor

School of Sciences, University of Southern Queensland, Toowoomba 4350, Australia
Interests: data mining; data analytics; health informatics

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) technologies have been widely applied in different areas in the recent decade. Particularly, the field of Electroencephalogram (EEG) Data Research and Healthcare Applications shows encouraging signs that AI is being increasingly considered for monitoring and predictive applications. EEG data analysis contains several challenges, including unwanted signal removing from raw EEG data; data processing, feature extraction and build classification model for abnormality recognition. The model building based on EEG is not robust in different cases for recognition of abnormalities. Although the automation of diagnosis procedures based on EEG data for various healthcare problems may help in improving patient care and overall healthcare, clinicians still play a significant role in understanding complex medical data for the diagnosis of diseases. Therefore, significant research is necessary to explore how AI technologies can be applied in human-brain signal analysis and healthcare applications to improve detective and predictive performance and support clinical diagnosis. Handling big data issues in EEG data processing is also necessary to explore in healthcare research.
This Special Issue will provide a forum for high-quality contributions in modeling, design, and application of AI to all aspects of Electroencephalogram Data research and Healthcare Applications.

Dr. Tianning Li
Prof. Dr. Xianpeng Wang
Dr. Siuly Siuly
Dr. Xiaohui Tao
Guest Editors

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Keywords

Electroencephalography
Signal processing
Healthcare applications
Artificial intelligence in EEG research
Machine learning techniques in EEG data analysis
Application of deep learning techniques
AI methods in health and medicine
Medical data mining and data analysis
Computer-aided diagnosis systems

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Published Papers (7 papers)

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Research

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17 pages, 2318 KiB

Open AccessArticle

Emotion Classification from Multi-Band Electroencephalogram Data Using Dynamic Simplifying Graph Convolutional Network and Channel Style Recalibration Module

by Xiaoliang Zhu, Gendong Liu, Liang Zhao, Wenting Rong, Junyi Sun and Ran Liu

Sensors 2023, 23(4), 1917; https://doi.org/10.3390/s23041917 - 8 Feb 2023

Cited by 5 | Viewed by 2332

Abstract

Because of its ability to objectively reflect people’s emotional states, electroencephalogram (EEG) has been attracting increasing research attention for emotion classification. The classification method based on spatial-domain analysis is one of the research hotspots. However, most previous studies ignored the complementarity of information between different frequency bands, and the information in a single frequency band is not fully mined, which increases the computational time and the difficulty of improving classification accuracy. To address the above problems, this study proposes an emotion classification method based on dynamic simplifying graph convolutional (SGC) networks and a style recalibration module (SRM) for channels, termed SGC-SRM, with multi-band EEG data as input. Specifically, first, the graph structure is constructed using the differential entropy characteristics of each sub-band and the internal relationship between different channels is dynamically learned through SGC networks. Second, a convolution layer based on the SRM is introduced to recalibrate channel features to extract more emotion-related features. Third, the extracted sub-band features are fused at the feature level and classified. In addition, to reduce the redundant information between EEG channels and the computational time, (1) we adopt only 12 channels that are suitable for emotion classification to optimize the recognition algorithm, which can save approximately 90.5% of the time cost compared with using all channels; (2) we adopt information in the θ, α, β, and γ bands, consequently saving 23.3% of the time consumed compared with that in the full bands while maintaining almost the same level of classification accuracy. Finally, a subject-independent experiment is conducted on the public SEED dataset using the leave-one-subject-out cross-validation strategy. According to experimental results, SGC-SRM improves classification accuracy by 5.51–15.43% compared with existing methods. Full article

(This article belongs to the Special Issue Electroencephalogram Data Research Using Artificial Intelligence Technologies for Healthcare)

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18 pages, 4120 KiB

Open AccessArticle

Influence of Channel Selection and Subject’s Age on the Performance of the Single Channel EEG-Based Automatic Sleep Staging Algorithms

by Waleed Nazih, Mostafa Shahin, Mohamed I. Eldesouki and Beena Ahmed

Sensors 2023, 23(2), 899; https://doi.org/10.3390/s23020899 - 12 Jan 2023

Cited by 5 | Viewed by 2672

Abstract

The electroencephalogram (EEG) signal is a key parameter used to identify the different sleep stages present in an overnight sleep recording. Sleep staging is crucial in the diagnosis of several sleep disorders; however, the manual annotation of the EEG signal is a costly and time-consuming process. Automatic sleep staging algorithms offer a practical and cost-effective alternative to manual sleep staging. However, due to the limited availability of EEG sleep datasets, the reliability of existing sleep staging algorithms is questionable. Furthermore, most reported experimental results have been obtained using adult EEG signals; the effectiveness of these algorithms using pediatric EEGs is unknown. In this paper, we conduct an intensive study of two state-of-the-art single-channel EEG-based sleep staging algorithms, namely DeepSleepNet and AttnSleep, using a recently released large-scale sleep dataset collected from 3984 patients, most of whom are children. The paper studies how the performance of these sleep staging algorithms varies when applied on different EEG channels and across different age groups. Furthermore, all results were analyzed within individual sleep stages to understand how each stage is affected by the choice of EEG channel and the participants’ age. The study concluded that the selection of the channel is crucial for the accuracy of the single-channel EEG-based automatic sleep staging methods. For instance, channels O1-M2 and O2-M1 performed consistently worse than other channels for both algorithms and through all age groups. The study also revealed the challenges in the automatic sleep staging of newborns and infants (1–52 weeks). Full article

(This article belongs to the Special Issue Electroencephalogram Data Research Using Artificial Intelligence Technologies for Healthcare)

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21 pages, 4303 KiB

Open AccessArticle

Impact of EEG Frequency Bands and Data Separation on the Performance of Person Verification Employing Neural Networks

by Renata Plucińska, Konrad Jędrzejewski, Marek Waligóra, Urszula Malinowska and Jacek Rogala

Sensors 2022, 22(15), 5529; https://doi.org/10.3390/s22155529 - 25 Jul 2022

Cited by 4 | Viewed by 3195

Abstract

The paper is devoted to the study of EEG-based people verification. Analyzed solutions employed shallow artificial neural networks using spectral EEG features as input representation. We investigated the impact of the features derived from different frequency bands and their combination on verification results. Moreover, we studied the influence of a number of hidden neurons in a neural network. The datasets used in the analysis consisted of signals recorded during resting state from 29 healthy adult participants performed on different days, 20 EEG sessions for each of the participants. We presented two different scenarios of training and testing processes. In the first scenario, we used different parts of each recording session to create the training and testing datasets, and in the second one, training and testing datasets originated from different recording sessions. Among single frequency bands, the best outcomes were obtained for the beta frequency band (mean accuracy of 91 and 89% for the first and second scenarios, respectively). Adding the spectral features from more frequency bands to the beta band features improved results (95.7 and 93.1%). The findings showed that there is not enough evidence that the results are different between networks using different numbers of hidden neurons. Additionally, we included results for the attack of 23 external impostors whose recordings were not used earlier in training or testing the neural network in both scenarios. Another significant finding of our study shows worse sensitivity results in the second scenario. This outcome indicates that most of the studies presenting verification or identification results based on the first scenario (dominating in the current literature) are overestimated when it comes to practical applications. Full article

(This article belongs to the Special Issue Electroencephalogram Data Research Using Artificial Intelligence Technologies for Healthcare)

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13 pages, 2094 KiB

Open AccessArticle

An Unsupervised Deep-Transfer-Learning-Based Motor Imagery EEG Classification Scheme for Brain–Computer Interface

by Xuying Wang, Rui Yang and Mengjie Huang

Sensors 2022, 22(6), 2241; https://doi.org/10.3390/s22062241 - 14 Mar 2022

Cited by 26 | Viewed by 4678

Abstract

Brain–computer interface (BCI) research has attracted worldwide attention and has been rapidly developed. As one well-known non-invasive BCI technique, electroencephalography (EEG) records the brain’s electrical signals from the scalp surface area. However, due to the non-stationary nature of the EEG signal, the distribution of the data collected at different times or from different subjects may be different. These problems affect the performance of the BCI system and limit the scope of its practical application. In this study, an unsupervised deep-transfer-learning-based method was proposed to deal with the current limitations of BCI systems by applying the idea of transfer learning to the classification of motor imagery EEG signals. The Euclidean space data alignment (EA) approach was adopted to align the covariance matrix of source and target domain EEG data in Euclidean space. Then, the common spatial pattern (CSP) was used to extract features from the aligned data matrix, and the deep convolutional neural network (CNN) was applied for EEG classification. The effectiveness of the proposed method has been verified through the experiment results based on public EEG datasets by comparing with the other four methods. Full article

(This article belongs to the Special Issue Electroencephalogram Data Research Using Artificial Intelligence Technologies for Healthcare)

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13 pages, 2074 KiB

Open AccessArticle

A Novel Permutation Entropy-Based EEG Channel Selection for Improving Epileptic Seizure Prediction

by Jee S. Ra, Tianning Li and Yan Li

Sensors 2021, 21(23), 7972; https://doi.org/10.3390/s21237972 - 29 Nov 2021

Cited by 29 | Viewed by 3832

Abstract

The key research aspects of detecting and predicting epileptic seizures using electroencephalography (EEG) signals are feature extraction and classification. This paper aims to develop a highly effective and accurate algorithm for seizure prediction. Efficient channel selection could be one of the solutions as it can decrease the computational loading significantly. In this research, we present a patient-specific optimization method for EEG channel selection based on permutation entropy (PE) values, employing K nearest neighbors (KNNs) combined with a genetic algorithm (GA) for epileptic seizure prediction. The classifier is the well-known support vector machine (SVM), and the CHB-MIT Scalp EEG Database is used in this research. The classification results from 22 patients using the channels selected to the patient show a high prediction rate (average 92.42%) compared to the SVM testing results with all channels (71.13%). On average, the accuracy, sensitivity, and specificity with selected channels are improved by 10.58%, 23.57%, and 5.56%, respectively. In addition, four patient cases validate over 90% accuracy, sensitivity, and specificity rates with just a few selected channels. The corresponding standard deviations are also smaller than those used by all channels, demonstrating that tailored channels are a robust way to optimize the seizure prediction. Full article

(This article belongs to the Special Issue Electroencephalogram Data Research Using Artificial Intelligence Technologies for Healthcare)

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19 pages, 1620 KiB

Open AccessArticle

Silent EEG-Speech Recognition Using Convolutional and Recurrent Neural Network with 85% Accuracy of 9 Words Classification

by Darya Vorontsova, Ivan Menshikov, Aleksandr Zubov, Kirill Orlov, Peter Rikunov, Ekaterina Zvereva, Lev Flitman, Anton Lanikin, Anna Sokolova, Sergey Markov and Alexandra Bernadotte

Sensors 2021, 21(20), 6744; https://doi.org/10.3390/s21206744 - 11 Oct 2021

Cited by 43 | Viewed by 8164

Abstract

In this work, we focus on silent speech recognition in electroencephalography (EEG) data of healthy individuals to advance brain–computer interface (BCI) development to include people with neurodegeneration and movement and communication difficulties in society. Our dataset was recorded from 270 healthy subjects during silent speech of eight different Russia words (commands): ‘forward’, ‘backward’, ‘up’, ‘down’, ‘help’, ‘take’, ‘stop’, and ‘release’, and one pseudoword. We began by demonstrating that silent word distributions can be very close statistically and that there are words describing directed movements that share similar patterns of brain activity. However, after training one individual, we achieved 85% accuracy performing 9 words (including pseudoword) classification and 88% accuracy on binary classification on average. We show that a smaller dataset collected on one participant allows for building a more accurate classifier for a given subject than a larger dataset collected on a group of people. At the same time, we show that the learning outcomes on a limited sample of EEG-data are transferable to the general population. Thus, we demonstrate the possibility of using selected command-words to create an EEG-based input device for people on whom the neural network classifier has not been trained, which is particularly important for people with disabilities. Full article

(This article belongs to the Special Issue Electroencephalogram Data Research Using Artificial Intelligence Technologies for Healthcare)

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Review

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23 pages, 415 KiB

Open AccessReview

Database and AI Diagnostic Tools Improve Understanding of Lung Damage, Correlation of Pulmonary Disease and Brain Damage in COVID-19

by Ilona Karpiel, Ana Starcevic and Mirella Urzeniczok

Sensors 2022, 22(16), 6312; https://doi.org/10.3390/s22166312 - 22 Aug 2022

Cited by 4 | Viewed by 4873

Abstract

The COVID-19 pandemic caused a sharp increase in the interest in artificial intelligence (AI) as a tool supporting the work of doctors in difficult conditions and providing early detection of the implications of the disease. Recent studies have shown that AI has been successfully applied in the healthcare sector. The objective of this paper is to perform a systematic review to summarize the electroencephalogram (EEG) findings in patients with coronavirus disease (COVID-19) and databases and tools used in artificial intelligence algorithms, supporting the diagnosis and correlation between lung disease and brain damage, and lung damage. Available search tools containing scientific publications, such as PubMed and Google Scholar, were comprehensively evaluated and searched with open databases and tools used in AI algorithms. This work aimed to collect papers from the period of January 2019–May 2022 including in their resources the database from which data necessary for further development of algorithms supporting the diagnosis of the respiratory system can be downloaded and the correlation between lung disease and brain damage can be evaluated. The 10 articles which show the most interesting AI algorithms, trained by using open databases and associated with lung diseases, were included for review with 12 articles related to EEGs, which have/or may be related with lung diseases. Full article

(This article belongs to the Special Issue Electroencephalogram Data Research Using Artificial Intelligence Technologies for Healthcare)

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