Article
Biology
Noor Kamal Al-Qazzaz, Zaid Abdi Alkareem Alyasseri, Karrar Hameed Abdulkareem, Nabeel Salih Ali, Mohammed Nasser Al-Mhiqani, Christoph Guger
Summary: This study introduces a new BCI feature fusion method and develops an automatic MI framework for detecting changes pre- and post-rehabilitation. The AICA-WT-TEF framework shows significant performance in MI rehabilitation for post-stroke patients, outperforming other classifiers.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Biology
Huiying Li, Dongxue Zhang, Jingmeng Xie
Summary: A novel dual-attention-based adversarial network for motor imagery classification (MI-DABAN) is proposed, which leverages multiple subjects' knowledge to improve a single subject's classification performance. The method employs a clever adversarial learning method and two unshared attention blocks, resulting in effective and superior classification performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Dongxue Zhang, Huiying Li, Jingmeng Xie
Summary: Due to its convenience and safety, EEG data is widely used in MI-BCIs. Recent research has begun applying Transformer to EEG signal decoding, but the challenge lies in effectively using data from other subjects to improve single-subject classification performance. To address this, we propose a novel architecture called MI-CAT, which utilizes Transformer's self-attention and cross-attention mechanisms to resolve the distribution differences between domains. Our method achieves competitive performance on real EEG datasets, demonstrating its effectiveness in decoding EEG signals and advancing the development of Transformer for BCIs.
Review
Chemistry, Analytical
Arrigo Palumbo, Vera Gramigna, Barbara Calabrese, Nicola Ielpo
Summary: Innovative aids, devices, and assistive technologies are needed to help individuals with severe disabilities live independently and improve overall health. Brain-Computer Interfaces using EEG data show potential for enhancing wheelchair control and movement in people with significant health challenges.
Article
Computer Science, Artificial Intelligence
Sion An, Soopil Kim, Philip Chikontwe, Sang Hyun Park
Summary: Recently, deep learning-based motor imagery (MI) electroencephalography (EEG) classification techniques have shown improved performance over conventional methods. However, accurately classifying unseen subjects remains challenging due to intersubject variability, scarcity of labeled data, and low signal-to-noise ratio (SNR). In this study, we propose a two-way few-shot network that efficiently learns representative features and performs classification with limited MI EEG data.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Review
Chemistry, Analytical
Aurora Saibene, Mirko Caglioni, Silvia Corchs, Francesca Gasparini
Summary: In recent decades, there has been significant growth in the automatic recognition and interpretation of brain waves through EEG technologies, leading to the development of non-invasive brain-computer interfaces (BCIs) that allow communication between humans and external devices. This paper presents a systematic review of EEG-based BCIs, with a focus on the promising paradigm of motor imagery (MI) and the use of wearable devices. The review assesses the maturity levels of these systems from technological and computational perspectives, and also provides a comprehensive list of experimental paradigms and available datasets for benchmarking and guiding the development of new applications and computational models.
Article
Engineering, Biomedical
Sergio Perez-Velasco, Eduardo Santamaria-Vazquez, Victor Martinez-Cagigal, Diego Marcos-Martinez, Roberto Hornero
Summary: In this study, a new Deep Learning architecture called EEGSym is proposed for improving the classification performance of Motor Imagery based Brain Computer Interfaces. EEGSym incorporates symmetry, data augmentation, and transfer learning to achieve higher accuracy and BCI control compared to baseline models. The experiments conducted on multiple datasets demonstrate the superior performance of EEGSym in terms of accuracy and BCI control. The results highlight the potential of EEGSym for practical applications in the field of Brain Computer Interfaces.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2022)
Article
Chemistry, Analytical
Ghadir Ali Altuwaijri, Ghulam Muhammad
Summary: This paper proposes a method to improve the classification accuracy of EEG motor imagery by using a multibranch CNN model to extract spatial and temporal features from raw EEG data. Experimental results demonstrate the superior performance of the proposed method compared to other state-of-the-art EEG classification methods.
Article
Computer Science, Information Systems
Bastian Hougaard, Hendrik Knoche, Mathias Sand Kristensen, Mads Jochumsen
Summary: Brain-computer interfaces (BCIs) can be used for stroke rehabilitation, but low performance can decrease control and frustrate users. Introducing fabricated input can improve control and reduce frustration, especially in game-based interactions and simple tasks.
Article
Computer Science, Artificial Intelligence
Dongxue Zhang, Huiying Li, Jingmeng Xie, Dajun Li
Summary: In this study, we propose MI-DAGSC, a model that addresses domain adaptation challenges in EEG-based motor imagery decoding. By combining multiple types of information and loss functions, the model effectively aligns the feature distributions of source and target domains, improving representation learning.
Article
Engineering, Biomedical
Lie Yang, Yonghao Song, Ke Ma, Enze Su, Longhan Xie
Summary: The proposed motor imagery EEG decoding method based on feature separation effectively improves the decoding accuracy by separating class-related features and class-independent features using the FSNAL network. Experimental results show that the method outperforms other state-of-the-art methods on public EEG datasets, demonstrating its potential for improving the performance of motor imagery BCI systems in the future.
JOURNAL OF NEURAL ENGINEERING
(2021)
Article
Mathematical & Computational Biology
Jiacan Xu, Donglin Li, Peng Zhou, Chunsheng Li, Zinan Wang, Shenghao Tong
Summary: This study proposes a multi-band centroid contrastive reconstruction fusion network (MB-CCRF) to address the feature fusion issue in motor imagery brain-computer interface. The experimental results show that this method achieves high accuracy on the BCI competition dataset and reveals the importance of different sub-band features for the MI classification task.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2023)
Article
Engineering, Electrical & Electronic
Samrudhi Mohdiwale, Mridu Sahu, G. R. Sinha, Vikrant Bhateja
Summary: BCI is not only beneficial for individuals with physical disabilities, but is also widely used in various applications. MI classification, focusing on EEG signal segments within specific frequency bands, is a significant contribution in BCI research. Feature selection, especially using the harmony search algorithm, plays a crucial role in improving the performance of MI classification.
IEEE SENSORS JOURNAL
(2021)
Article
Computer Science, Information Systems
L. Ferrero, M. Ortiz, V. Quiles, E. Ianez, J. M. Azorin
Summary: Motor imagery is a common paradigm in brain-computer interfaces, and virtual reality training can enhance the effectiveness of gait imagery. Visual feedback through VR led to higher performances compared to screen feedback, regardless of whether subjects were seated or standing.
Article
Engineering, Biomedical
Hongli Li, Man Ding, Ronghua Zhang, Chunbo Xiu
Summary: A neural network feature fusion algorithm combining CNN and LSTM has been proposed to improve the accuracy of motor imagery EEG classification, providing new ideas for related research. The average accuracy and Kappa value were found to be 87.68% and 0.8245, respectively.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Engineering, Biomedical
Wenwen Wu, Yanqi Huang, Xiaomei Wu
Summary: In this study, a 2D deep learning classification network SRT was proposed to improve automatic ECG analysis. The model structure was enhanced with the CNN and Transformer-encoder modules, and a novel attention module and Dilated Stem structure were introduced to improve feature extraction. Comparative experiments showed that the proposed model outperformed several advanced methods.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Chiheb Jamazi, Ghaith Manita, Amit Chhabra, Houssem Manita, Ouajdi Korbaa
Summary: In this study, a new dynamic and intelligent clustering method for brain tumor segmentation is proposed by combining the improved Aquila Optimizer (AO) and the K-Means algorithm. The proposed MAO-Kmeans approach aims to automatically extract the correct number and location of cluster centers and the number of pixels in each cluster in abnormal MRI images, and the experimental results demonstrate its effectiveness in improving the performance of conventional K-means clustering.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Alberto Hernando, Maria Dolores Pelaez-Coca, Eduardo Gil
Summary: This study applied a new algorithm to decompose the photoplethysmogram (PPG) pulse and identified changes in PPG pulse morphology due to pressure. The results showed that there was an increase in amplitude, width, and area values of the PPG pulse, and a decrease in ratios when pressure increased, indicating vasoconstriction. Furthermore, some parameters were found to be related to the pulse-to-pulse interval.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Jens Moeller, Eveline Popanda, Nuri H. Aydin, Hubert Welp, Iris Tischoff, Carsten Brenner, Kirsten Schmieder, Martin R. Hofmann, Dorothea Miller
Summary: In this study, a method based on texture features is proposed, which can classify healthy gray and white matter against glioma degrees 4 samples with reasonable classification performance using a relatively low number of samples for training. The method achieves high classification performance without the need for large datasets and complex machine learning approaches.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Amrutha Bhaskaran, Manish Arora
Summary: The study evaluates a cyclic repetition frequency-based algorithm for fetal heart rate estimation. The algorithm improves accuracy and reliability for poor-quality signals and performs well for different gestation weeks and clinical settings.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Manan Patel, Harsh Bhatt, Manushi Munshi, Shivani Pandya, Swati Jain, Priyank Thakkar, Sangwon Yoon
Summary: Electroencephalogram (EEG) signals have been effectively used to measure and analyze neurological data and brain-related ailments. Artificial Intelligence (AI) algorithms, specifically the proposed CNN-FEBAC framework, show promising results in studying the EEG signals of autistic patients and predicting their response to stimuli with 91% accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Wencheng Gu, Kexue Sun
Summary: This research proposes an improved version of YOLOv5 (AYOLOv5) based on the attention mechanism to address the issue of low recognition rate in cell detection. Experimental results demonstrate that AYOLOv5 can accurately identify cell targets and improve the quality and recognition performance of cell pictures.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Anita Gade, V. Vijaya Baskar, John Panneerselvam
Summary: Analysis of exhaled breath is an increasingly used diagnostic technique in medicine. This study introduces a new NICBGM-based model that utilizes various features and weight optimization for accurate data interpretation and result optimization.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Arsalan Asemi, Keivan Maghooli, Fereidoun Nowshiravan Rahatabad, Hamid Azadeh
Summary: Biometric authentication systems can perform identity verification with optimal accuracy in various environments and emotional changes, while the performance of signature verification systems can be affected when people are under stress. This study examines the performance of a signature verification system based on muscle synergy patterns as biometric characteristics for stressed individuals. EMG signals from hand and arm muscles were recorded and muscle synergies were extracted using Non-Negative Matrix Factorization. The extracted patterns were classified using Support Vector Machine for authentication of stressed individuals.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Tianjiao Guo, Jie Yang, Qi Yu
Summary: This paper proposes a CNN-based approach for segmenting four typical DR lesions simultaneously, achieving competitive performance. This approach is significant for DR lesion segmentation and has potential in other segmentation tasks.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
G. Akilandasowmya, G. Nirmaladevi, S. U. Suganthi, A. Aishwariya
Summary: This study proposes a technique for skin cancer detection and classification using deep hidden features and ensemble classifiers. By optimizing features to reduce data dimensionality and combining ensemble classifiers, the proposed method outperforms in skin cancer classification and improves prediction accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Tuuli Uudeberg, Juri Belikov, Laura Paeske, Hiie Hinrikus, Innar Liiv, Maie Bachmann
Summary: This article introduces a novel feature extraction method, the in-phase matrix profile (pMP), specifically adapted for electroencephalographic (EEG) signals, for detecting major depressive disorder (MDD). The results show that pMP outperforms Higuchi's fractal dimension (HFD) in detecting MDD, making it a promising method for future studies and potential clinical use for diagnosing MDD.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
P. Nancy, M. Parameswari, J. Sathya Priya
Summary: Stroke is the third leading cause of mortality worldwide, and early detection is crucial to avoid health risks. Existing research on disease detection using machine learning techniques has limitations, so a new stroke detection system is proposed. The experimental results show that the proposed method achieves a high accuracy rate in stroke detection.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Shimin Liu, Zhiwen Huang, Jianmin Zhu, Baolin Liu, Panyu Zhou
Summary: In this study, a continuous blood pressure (BP) monitoring method based on random forest feature selection (RFFS) and a gray wolf optimization-gradient boosting regression tree (GWO-GBRT) prediction model was developed. The method extracted features from electrocardiogram (ECG) and photoplethysmography (PPG) signals, and employed RFFS to select sensitive features highly correlated with BP. A hybrid prediction model of gray wolf optimization (GWO) technique and gradient boosting regression tree (GBRT) algorithm was established to learn the relationship between BP and sensitive features. Experimental results demonstrated the effectiveness and advancement of the proposed method.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Weijun Gong, Yurong Qian, Weihang Zhou, Hongyong Leng
Summary: The recognition of dynamic facial expressions is challenging due to various factors, and obtaining discriminative expression features has been difficult. Traditional deep learning networks lack understanding of global and temporal expressions. This study proposes an enhanced spatial-temporal learning network to improve dynamic facial expression recognition.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)