Biocybernetics and Biomedical Engineering最新文献

{"title":"Imaging of retinal ganglion cells and photoreceptors using Spatio-Temporal Optical Coherence Tomography (STOC-T) without hardware-based adaptive optics","authors":"Marta Mikuła-Zdańkowska ,&nbsp;Dawid Borycki ,&nbsp;Piotr Węgrzyn ,&nbsp;Karolis Adomavičius ,&nbsp;Egidijus Auksorius ,&nbsp;Maciej Wojtkowski","doi":"10.1016/j.bbe.2025.01.001","DOIUrl":"10.1016/j.bbe.2025.01.001","url":null,"abstract":"<div><div>We demonstrate an experimental Spatio-Temporal Optical Coherence Tomography (STOC-T) system featuring optimized illumination and an increased lateral resolution of approximately 3 <!--> <!-->µm. The integration of high-speed phase randomization with a numerical averaging process facilitates a noticeable improvement in the signal-to-noise ratio. The effectiveness of this enhancement is demonstrated through volumetric imaging of a scattering object, and it enables <em>in vivo</em> imaging of the human retina at the cellular level. Additionally, the experiment is supported by computational aberration-correction techniques to achieve high-resolution <em>in vivo</em> imaging of the human retina. The visualization of retinal cone mosaics, and ganglion cell somas was achieved through contrast enhancement during the averaging process.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 1","pages":"Pages 52-61"},"PeriodicalIF":5.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatio-temporal matched filter adjustment for enhanced accuracy in brain responses classification","authors":"Michal Piela,&nbsp;Marian P. Kotas","doi":"10.1016/j.bbe.2024.12.003","DOIUrl":"10.1016/j.bbe.2024.12.003","url":null,"abstract":"<div><div>In this paper, we apply modified spatio-temporal matched filtering (MSTMF) to enhance electroencephalographic (EEG) signals in evoked potentials (EP) based brain–computer interfaces (BCI). Our focus is on the effective treatment of noise in the system under consideration.</div><div>The applied MSTMF is a spatio-temporal extension of generalized matched filtering, which allows for optimal enhancement of weak, repeatable signals embedded in colored Gaussian noise. However, since spontaneous EEG signals are often corrupted by high-energy super-Gaussian artifacts, which deviate from this distribution, we propose rejecting these artifacts before applying MSTMF. Particularly effective have been algorithms based on independent component analysis (ICA) and empirical mode decomposition (EMD). After artifacts rejection, performed locally within time segments they occupy, without disturbing other parts of the signal, the classification of brain responses becomes more accurate. Nevertheless, the nonstationarity of the EEG signal remains a challenge that must be addressed.</div><div>Therefore, we propose adjusting the MSTMF to the current noise properties to improve its performance in this demanding environment. This can be achieved by properly calculating the noise covariance matrix, which is necessary to determine the filter coefficients, using both the learning and currently processed signal segments.</div><div>As a result, we have developed an enhanced method based on MSTMF for improved discrimination of evoked potentials and verified its performance on two publicly available reference databases: BCIAUT-P300 (for IFMBE Scientific Challenge) and Speller (for the BCI Competition III Challenge 2004). For these databases, we have achieved overall accuracies of 92.67% and 99.5%, surpassing the reference methods presented in the literature.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 1","pages":"Pages 34-51"},"PeriodicalIF":5.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing eye disease detection: A comprehensive study on computer-aided diagnosis with vision transformers and SHAP explainability techniques","authors":"Hossam Magdy Balaha ,&nbsp;Asmaa El-Sayed Hassan ,&nbsp;Rawan Ayman Ahmed ,&nbsp;Magdy Hassan Balaha","doi":"10.1016/j.bbe.2024.11.005","DOIUrl":"10.1016/j.bbe.2024.11.005","url":null,"abstract":"<div><div>Eye diseases such as age-related macular degeneration (AMD) and diabetic retinopathy are common worldwide and affect millions of people. These conditions can cause severe vision problems and even lead to blindness if not treated promptly. Therefore, accurate and timely diagnosis is crucial to manage these diseases effectively and prevent irreversible vision loss. This study introduces a computer-aided diagnosis (CAD) framework for automatically detecting various eye diseases via advanced methodologies and datasets. The main focus is on classifying fundus images, which is essential for precise diagnosis and prognosis. By incorporating cutting-edge techniques such as Vision Transformers (ViTs), this study aims to improve the performance and interpretability of traditional Convolutional Neural Networks (CNNs). ViTs can capture complex patterns and long-range dependencies in fundus images, helping distinguish between different eye diseases and healthy conditions. Furthermore, the study integrates SHapley additive exPlanations (SHAP) explainability techniques to provide insights into the model’s decision-making process, enhancing trust and understanding of its predictions. The results demonstrate significant performance enhancements compared with the baseline models, with an overall accuracy of 95%. This method outperforms previous state-of-the-art methods by a considerable margin. Additionally, metrics such as precision, recall, intersection over union (IoU), and the Matthews correlation coefficient (MCC) show superior performance across various eye diseases, such as diabetic retinopathy, glaucoma, and age-related macular degeneration. These findings underscore the effectiveness and reliability of the proposed approach in automated eye disease detection, indicating its potential for clinical integration and widespread adoption in healthcare settings.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 1","pages":"Pages 23-33"},"PeriodicalIF":5.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-scale neural networks classification of mild cognitive impairment using functional near-infrared spectroscopy","authors":"Min-Kyoung Kang ,&nbsp;Keum-Shik Hong ,&nbsp;Dalin Yang ,&nbsp;Ho Kyung Kim","doi":"10.1016/j.bbe.2024.12.001","DOIUrl":"10.1016/j.bbe.2024.12.001","url":null,"abstract":"<div><div>Mild cognitive impairment (MCI) is recognized as an early stage preceding Alzheimer’s disease. Functional near-infrared spectroscopy (fNIRS) has recently been used to differentiate MCI patients from healthy controls (HCs) by analyzing their hemodynamic responses. This paper proposes a new method that uses the entire time series data from all fNIRS channels, skipping the feature extraction step. It involves a multi-scale convolutional neural network (CNN) integrated with long short-term memory (LSTM) layers to extract spatial and temporal features simultaneously. The study involves 64 participants (37 MCI patients and 27 HCs) performing three mental tasks: <em>N</em>-back, Stroop, and verbal fluency tests (VFT). The algorithm’s performance was assessed using 10-fold cross-validation across oxyhemoglobin (HbO), deoxyhemoglobin (HbR), and total hemoglobin (HbT). The highest classification accuracies were achieved with HbT, reaching 93.22 % for the <em>N</em>-back task, 91.14 % for the Stroop task, and 89.58 % for the VFT. It was found that using all types of hemodynamic signals from all channels provides better results than analyzing the region of interest data, eliminating the need for data segmentation and feature extraction procedures. Additionally, HbR (or HbT) gives better classification accuracy than HbO. The developed method can be implemented online for clinical applications and real-time monitoring of cognitive disorders.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 1","pages":"Pages 11-22"},"PeriodicalIF":5.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two state quasi-LPV dynamic model for gas exchange dynamics using the cycle-ergometer test","authors":"J.D. Chiza-Ocaña ,&nbsp;G. Realpe ,&nbsp;C.A. López-Albán ,&nbsp;E. Rosero ,&nbsp;J.M. Ramírez-Scarpetta","doi":"10.1016/j.bbe.2025.01.005","DOIUrl":"10.1016/j.bbe.2025.01.005","url":null,"abstract":"<div><div>This paper presents a two state quasi-linear parameter varying <span><math><mrow><mo>(</mo><mi>q</mi><mi>u</mi><mi>a</mi><mi>s</mi><mi>i</mi><mo>−</mo><mi>L</mi><mi>P</mi><mi>V</mi><mo>)</mo></mrow></math></span> dynamic model for gas exchange dynamics using the cycle-ergometer test. The obtained model, is based on the analysis of stationary and dynamic energy flow, and the <span><math><mrow><mi>V</mi><mo>−</mo><mi>s</mi><mi>l</mi><mi>o</mi><mi>p</mi><mi>e</mi></mrow></math></span> method analysis, applies to both oxidative and glycolytic physical activities performed by an individual. The model parameters were identified by a power meter measuring the mechanical power at the pedal level on an ergometer bicycle (input signal), a commercial gas analyzer measuring the flow of oxygen uptake and the flow of carbon dioxide excreted (output signals), with data generated from two test protocols: a mixed protocol and an incremental cycling protocol. The model’s parameters are obtained in parts, from the measurements taken in the oxidative stage, the glycolytic stage, and the transition stage between the two, using the mixed protocol. The resulting model is validated using data from the incremental cycling protocol of nine individuals: six males and three females. The validated models obtained an accuracy of above 84.8% for the flow of oxygen and 89.1% for the flow of carbon dioxide. The dynamic model could be used to aid in creating personalized physical exercise programs for overweight individuals, simulating training plans within the operational thresholds of the human body or in structuring high performance training for athletes.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 1","pages":"Pages 105-113"},"PeriodicalIF":5.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel copolymers of poly(sebacic anhydride) and poly(ethylene glycol) as azithromycin carriers to the lungs","authors":"Konrad Kwiecień ,&nbsp;Karolina Knap ,&nbsp;Rick Heida ,&nbsp;Jonasz Czajkowski ,&nbsp;Alan Gorter ,&nbsp;Dorota Ochońska ,&nbsp;Przemysław Mielczarek ,&nbsp;Agata Dorosz ,&nbsp;Daria Niewolik ,&nbsp;Katarzyna Reczyńska-Kolman ,&nbsp;Katarzyna Jaszcz ,&nbsp;Monika Brzychczy-Włoch ,&nbsp;Tomasz R. Sosnowski ,&nbsp;Peter Olinga ,&nbsp;Elżbieta Pamuła","doi":"10.1016/j.bbe.2025.01.002","DOIUrl":"10.1016/j.bbe.2025.01.002","url":null,"abstract":"<div><div>By many chronic lung diseases, there is a problem of recurrent bacterial infections that require frequent usage of antibiotics. They can be more effective and cause fewer side effects when administrated directly via the pulmonary route. For such purposes, various types of inhalers are used of which dry powder inhalers (DPIs) are one of the most common. Formulations such as dry powders usually consist of an active pharmaceutical ingredient (API) and a carrier material that is supposed to provide adequate properties to deliver the bioactive molecules to the site of action, effectively. Copolymers of sebacic acid (SA) and poly(ethylene glycol) (PEG) have been regarded as suitable materials for such formulations. Here, we present a study about the manufacturing of microparticles from such materials dedicated to inhalation which have been loaded with azithromycin (AZM). The microparticles (MPs) were 0.5 to 5 µm in size, presenting either a spherical or elongated shape depending on the material type and composition. The encapsulation efficiency (EE) of the MPs were almost complete with the drug loading up to 23.1 %. The powders had fair or good flowability based on Carr’s index and Hausner ratio. Due to the presence of the drug, the tendency to agglomerate decreased. As a result, up to 90 % of the obtained powders showed diameters below 5 µm. Also, the fine particles fraction (FPF) of the chosen formulation reached 66.3 ± 4.5 % and the mass median aerodynamic diameter was 3.8 ± 0.4 µm. The microparticles degraded quickly <em>in vitro</em> losing up to 50 % of their mass within 24 h and up to 80 % within 96 h of their incubation in phosphate-buffered saline (PBS). They were also nontoxic up to 100 µg/ml when added to cultures of A549 and BEAS-2B lung epithelial cells as well as to rat lung tissue slices tested <em>ex vivo</em>. The microparticles showed bactericidal effects against various strains of <em>Staphylococcus aureus</em> in lower than cytotoxic concentrations.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 1","pages":"Pages 114-136"},"PeriodicalIF":5.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regional constraint consistency contrastive learning for automatic detection of urinary sediment in microscopic images","authors":"Fufei Li ,&nbsp;Li Chen ,&nbsp;Ge Song ,&nbsp;Lianzheng Su ,&nbsp;Shian Wang ,&nbsp;Qiuyue Fu ,&nbsp;Yongqi Nie ,&nbsp;Peng Wang","doi":"10.1016/j.bbe.2025.01.003","DOIUrl":"10.1016/j.bbe.2025.01.003","url":null,"abstract":"<div><div>Diagnosing renal and urinary system illnesses usually entails analysing the sediment found in urine. The components in microscopic urine images are diverse and show high similarity, with low contrast due to noise, impeding the progress of automated urine analysis. In order to tackle this difficulty, we propose a region-constrained consistency contrastive learning approach for automated urine analysis. In the first stage, we tackle the complex overlap phenomena in microscopic urine images by innovating the Urine Sediment Paste (US-Paste) positive sample construction method based on supervised contrastive learning. This method uses label information to apply regional constraints and improves the performance of out-of-distribution detection. We also rebuilt the Global Guidance Module (GG Module) and the Enhanced Supervision Module(ES Module). The former improves contrast in urine sediment images by restoring important image details guided by an encoder–decoder structure, while the latter achieves strong feature consistency by combining the most pertinent feature responses from four sets of attention feature maps, which are further mapped via a projection network. In the second phase, we enhance the representations acquired in the initial phase by incorporating a linear classification layer. Our region-constrained consistency contrastive learning algorithm attained an average classification accuracy of 98.30%, precision of 98.33%, recall of 98.30%, and F1-score of 98.30% on the private dataset. Furthermore, in the public urine sediment dataset, the approach achieved an average classification accuracy of 96.19%, precision of 95.79%, recall of 96.19%, and F1-score of 95.94%. The public chromosomal dataset yielded an average classification accuracy of 95.46%, precision of 94.84%, recall of 95.47%, and F1-score of 95.15%. Our methodology surpasses the most advanced methods and demonstrates exceptional performance in urine analysis. This showcases the efficiency of our label-based regional limitations, the outstanding out-of-distribution detection performance of US-Paste, and the robust feature consistency achieved by the Guided Supervision Encoder (GS Encoder). This substantially enhances diagnostic efficiency for clinicians and significantly advances the progress of automated urine sediment analysis.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 1","pages":"Pages 74-89"},"PeriodicalIF":5.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis on grading of lung nodule images with segmentation using u-net and classification with Convolutional Neural Network Fish Swarm Optimization","authors":"R. Sudha ,&nbsp;K.M. Uma Maheswari","doi":"10.1016/j.bbe.2024.12.002","DOIUrl":"10.1016/j.bbe.2024.12.002","url":null,"abstract":"<div><div>Lung malignant tumors are abnormal growths of cells in the lungs that have the potential to invade nearby tissues and spread to other parts of the body. Early detection of these malignant lung tumors is crucial to avoid complications and improve patient outcomes. However, manual processing consumes time and is a tedious process. This might result in poor estimation on cancer-prognosis, leading the patients into a higher risk of mortality. Many existing literatures have detected the malignant tumors, yet, found certain difficulties with the identification of size, appearance and spread of cancerous-cells in lung region to determine how far it has been occupied. Hence, the present study aims to overcome the existing complications through Deep Learning based Swarm Intelligence Algorithms. Implementation of the proposed work is involved with three stages such as pre-processing, segmentation and classification. Besides, CT scan possess the capability for giving a comprehensive view than X-rays. Data are collected from LIDC-IDRI (Lung Image Database Consortium-Image Database Resource Initiative) with lung CT-images and accomplishes pre-processing by removing noise efficiently using wiener filter. Further, changes in soft tissues of lungs are identified and segmented in the subsequent phase using U-Net and finally classification is performed using CFSO (Convolutional Neural Network Fish Swarm Optimization) to overcome the slight chance of misclassification error as proposed CFSO can lead to more efficient computational processes since FSO algorithms are designed to minimize computational costs while maximizing performance through their metaheuristic nature. This efficiency is particularly beneficial when dealing with large datasets typical in medical imaging, allowing faster processing times without sacrificing accuracy. Hence, amalgamation of CFSO can reduce the number of features, thus speeding up training and inference times. Through the performance assessment, IoU (Intersection over Union) value attained through the analysis is found to be 0.7822. Further, accuracy obtained by the proposed model is 97.80%, recall is 98.49%, precision is 96.8% and F1-score is 97.32%. Findings of the study exhibits the purposefulness of the study in clinical settings by potentially reducing false negatives in lung cancer screening, ultimately improving patient survival rates through earlier detection and treatment.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 1","pages":"Pages 90-104"},"PeriodicalIF":5.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilistic and explainable modeling of Phase–Phase Cross-Frequency Coupling patterns in EEG. Application to dyslexia diagnosis","authors":"Diego Castillo-Barnes ,&nbsp;Nicolás J. Gallego-Molina ,&nbsp;Marco A. Formoso ,&nbsp;Andrés Ortiz ,&nbsp;Patrícia Figueiredo ,&nbsp;Juan L. Luque","doi":"10.1016/j.bbe.2024.09.003","DOIUrl":"10.1016/j.bbe.2024.09.003","url":null,"abstract":"<div><div>This work explores the intricate neural dynamics associated with dyslexia through the lens of Cross-Frequency Coupling (CFC) analysis applied to electroencephalography (EEG) signals evaluated from 48 seven-year-old Spanish readers from the LEEDUCA research platform. The analysis focuses on CFS (Cross-Frequency phase Synchronization) maps, capturing the interaction between different frequency bands during low-level auditory processing stimuli. Then, making use of Gaussian Mixture Models (GMMs), CFS activations are quantified and classified, offering a compressed representation of EEG activation maps. The study unveils promising results specially at the Theta-Gamma coupling (Area Under the Curve = 0.821), demonstrating the method’s sensitivity to dyslexia-related neural patterns and highlighting potential applications in the early identification of dyslexic individuals.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 4","pages":"Pages 814-823"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of motor imagery EEG signals using wavelet scattering transform and Bi-directional long short-term memory networks","authors":"Hongyuan Zhang ,&nbsp;Zijian Zhao ,&nbsp;Chong Liu ,&nbsp;Miao Duan ,&nbsp;Zhiguo Lu ,&nbsp;Hong Wang","doi":"10.1016/j.bbe.2024.11.003","DOIUrl":"10.1016/j.bbe.2024.11.003","url":null,"abstract":"<div><div>A brain-computer interface (BCI) is a technology that creates a communication path between the brain and external devices. Raw EEG data in BCI contain a large amount of complex information, but only some of it needs to be focused on in research. So Feature extraction and classification play an important role in BCI by reducing the data dimensionality and improving the accuracy of subsequent classification. Wavelet scattering transform is an emerging feature extraction method that generates time-shift invariant representations of EEG signals. We applied the wavelet scattering transform to extract features from motor imagery EEG signals, and utilized these features for classification purposes. To achieve this, we proposed a new method that combines wavelet scattering transform with a bidirectional long short-term memory (BiLSTM) network in a fusion deep learning network. Wavelet scattering transform can deeply mine the feature information in EEG signals. In the classification stage, multiple time window features obtained in the scattering transform are sent to the BiLSTM network for classification. The final result will be determined by a vote. In addition, for the processing of raw EEG data, we proposed a time-step based time window strategy that can better utilize the small dataset. This operation can obtain EEG data of multiple time steps. The proposed method was validated using BCI competition II dataset III and BCI competition IV dataset 2b. The results show that the proposed method in this paper can effectively improve the accuracy of motor imagery EEG and provide a new idea for the feature extraction and classification research of motor imagery brain-computer interface.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 4","pages":"Pages 874-884"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143158887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}