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Special Issue "Electroencephalogram Data Research Using Artificial Intelligence Technologies for Healthcare"

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

Deadline for manuscript submissions: 31 May 2022 | Viewed by 3236

Special Issue Editors

Dr. Tianning Li
E-Mail Website
Guest Editor
School of Sciences, University of Southern Queensland, Toowoomba 4350, Australia
Interests: artificial intelligence; signal processing; EEG research
Prof. Dr. Xianpeng Wang
E-Mail Website
Guest Editor
State Key Laboratory of Marine Resource Utilization in South China Sea and School of Information and Communication Engineering, Hainan University, Haikou 570228, China
Interests: array signal processing; radar signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Siuly Siuly
E-Mail Website
Guest Editor
Institute of Sustainable Industries and Liveable Cities, Victoria University Melbourne, Melbourne, Australia
Interests: biomedical signal processing; epileptic seizure detection and prediction; brain-computer interface (BCI); artificial intelligence; data mining; pattern recognition for developing computer-aided analysis systems
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

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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

  • 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

Published Papers (3 papers)

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Research

Article
An Unsupervised Deep-Transfer-Learning-Based Motor Imagery EEG Classification Scheme for Brain–Computer Interface
Sensors 2022, 22(6), 2241; https://doi.org/10.3390/s22062241 - 14 Mar 2022
Viewed by 540
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 [...] Read more.
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
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Article
A Novel Permutation Entropy-Based EEG Channel Selection for Improving Epileptic Seizure Prediction
Sensors 2021, 21(23), 7972; https://doi.org/10.3390/s21237972 - 29 Nov 2021
Cited by 2 | Viewed by 481
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 [...] Read more.
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
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Article
Silent EEG-Speech Recognition Using Convolutional and Recurrent Neural Network with 85% Accuracy of 9 Words Classification
Sensors 2021, 21(20), 6744; https://doi.org/10.3390/s21206744 - 11 Oct 2021
Viewed by 1568
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 [...] Read more.
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
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