Physics-Informed Attention Temporal Convolutional Network for EEG-Based Motor Imagery Classification

IEEE Transactions on Industrial Informatics, Journal Year: 2022, Volume and Issue: 19(2), P. 2249 - 2258

Published: Aug. 9, 2022

The brain-computer interface (BCI) is a cutting-edge technology that has the potential to change world. Electroencephalogram (EEG) motor imagery (MI) signal been used extensively in many BCI applications assist disabled people, control devices or environments, and even augment human capabilities. However, limited performance of brain decoding restricting broad growth industry. In this article, we propose an attention-based temporal convolutional network (ATCNet) for EEG-based classification. ATCNet model utilizes multiple techniques boost MI classification with relatively small number parameters. employs scientific machine learning design domain-specific deep interpretable explainable features, multihead self-attention highlight most valuable features MI-EEG data, extract high-level convolutional-based sliding window data efficiently. proposed outperforms current state-of-the-art Competition IV-2a dataset accuracy 85.38% 70.97% subject-dependent subject-independent modes, respectively.

Language: Английский

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Towards a blockchain-SDN-based secure architecture for cloud computing in smart industrial IoT

Digital Communications and Networks, Journal Year: 2022, Volume and Issue: 9(2), P. 411 - 421

Published: Nov. 13, 2022

Some of the significant new technologies researched in recent studies include BlockChain (BC), Software Defined Networking (SDN), and Smart Industrial Internet Things (IIoT). All three provide data integrity, confidentiality, integrity their respective use cases (especially industrial fields). Additionally, cloud computing has been for several years now. Confidential information is exchanged with infrastructure to clients access distant resources, such as storage activities IIoT. There are also security risks, concerns, difficulties associated computing. To address these challenges, we propose merging BC SDN into a platform This paper introduces "DistB-SDCloud", an architecture enhanced smart IIoT applications. The proposed uses distributed method security, secrecy, privacy, while remaining flexible scalable. Customers sector benefit from dispersed or decentralized, efficient environment BC. described improve durability, stability, load balancing infrastructure. efficacy our BC-based implementation was experimentally tested by using various parameters including throughput, packet analysis, response time, bandwidth, latency well monitoring attacks on system itself.

Language: Английский

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Tuberculosis detection in chest radiograph using convolutional neural network architecture and explainable artificial intelligence

Neural Computing and Applications, Journal Year: 2022, Volume and Issue: 36(1), P. 111 - 131

Published: April 19, 2022

Language: Английский

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A Multi-Branch Convolutional Neural Network with Squeeze-and-Excitation Attention Blocks for EEG-Based Motor Imagery Signals Classification

Diagnostics, Journal Year: 2022, Volume and Issue: 12(4), P. 995 - 995

Published: April 15, 2022

Electroencephalography-based motor imagery (EEG-MI) classification is a critical component of the brain-computer interface (BCI), which enables people with physical limitations to communicate outside world via assistive technology. Regrettably, EEG decoding challenging because complexity, dynamic nature, and low signal-to-noise ratio signal. Developing an end-to-end architecture capable correctly extracting data's high-level features remains difficulty. This study introduces new model for MI known as Multi-Branch EEGNet squeeze-and-excitation blocks (MBEEGSE). By clearly specifying channel interdependencies, multi-branch CNN attention employed adaptively change channel-wise feature responses. When compared existing state-of-the-art models, suggested achieves good accuracy (82.87%) reduced parameters in BCI-IV2a dataset (96.15%) high gamma dataset.

Language: Английский

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A Model Combining Multi Branch Spectral-Temporal CNN, Efficient Channel Attention, and LightGBM for MI-BCI Classification

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Journal Year: 2023, Volume and Issue: 31, P. 1311 - 1320

Published: Jan. 1, 2023

Accurately decoding motor imagery (MI) brain-computer interface (BCI) tasks has remained a challenge for both neuroscience research and clinical diagnosis. Unfortunately, less subject information low signal-to-noise ratio of MI electroencephalography (EEG) signals make it difficult to decode the movement intentions users. In this study, we proposed an end-to-end deep learning model, multi-branch spectral-temporal convolutional neural network with channel attention LightGBM model (MBSTCNN-ECA-LightGBM), MI-EEG tasks. We first constructed multi branch CNN module learn domain features. Subsequently, added efficient mechanism obtain more discriminative Finally, was applied multi-classification The within-subject cross-session training strategy used validate classification results. experimental results showed that achieved average accuracy 86% on two-class MI-BCI data 74% four-class data, which outperformed current state-of-the-art methods. MBSTCNN-ECA-LightGBM can efficiently spectral temporal EEG, improving performance MI-based BCIs.

Language: Английский

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Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities

AIMS Public Health, Journal Year: 2024, Volume and Issue: 11(1), P. 58 - 109

Published: Jan. 1, 2024

<abstract> <p>In recent years, machine learning (ML) and deep (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given current progress fields of ML DL, there exists promising potential for both provide support realm healthcare. This study offered an exhaustive survey on DL system, concentrating vital state art features, integration benefits, applications, prospects future guidelines. To conduct research, we found most prominent journal conference databases using distinct keywords discover scholarly consequences. First, furnished along with cutting-edge ML-DL-based analysis smart a compendious manner. Next, integrated advancement services including ML-healthcare, DL-healthcare, ML-DL-healthcare. We then DL-based applications industry. Eventually, emphasized research disputes recommendations further studies based our observations.</p> </abstract>

Language: Английский

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EEGConvNeXt: A Novel Convolutional Neural Network Model for Automated Detection of Alzheimer's Disease and Frontotemporal Dementia Using EEG Signals

Computer Methods and Programs in Biomedicine, Journal Year: 2025, Volume and Issue: 262, P. 108652 - 108652

Published: Feb. 8, 2025

Language: Английский

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Attention-Inception and Long- Short-Term Memory-Based Electroencephalography Classification for Motor Imagery Tasks in Rehabilitation

IEEE Transactions on Industrial Informatics, Journal Year: 2021, Volume and Issue: 18(8), P. 5412 - 5421

Published: Dec. 3, 2021

In recent years, the contributions of deep learning have had a phenomenal impact on electroencephalography-based brain-computer interfaces. While decoding accuracy electroencephalography signals has continued to increase, process caused models continuously expand in terms size and computational resource requirements. However, due their increased requirements, it become difficult embed, store, execute for artificial intelligence things, cloud-based, or edge devices used rehabilitation. Hence, this article proposes novel learning-based lightweight model based attention-inception convolutional neural network long- short-term memory. The proposed achieves excellent public competition datasets while requiring few parameters low time. Using BCI IV 2a dataset high gamma dataset, achieved 82.8% 97.1% accuracies, respectively.

Language: Английский

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A Multibranch of Convolutional Neural Network Models for Electroencephalogram-Based Motor Imagery Classification

Biosensors, Journal Year: 2022, Volume and Issue: 12(1), P. 22 - 22

Published: Jan. 3, 2022

Automatic high-level feature extraction has become a possibility with the advancement of deep learning, and it been used to optimize efficiency. Recently, classification methods for Convolutional Neural Network (CNN)-based electroencephalography (EEG) motor imagery have proposed, achieved reasonably high accuracy. These approaches, however, use CNN single convolution scale, whereas best scale varies from subject subject. This limits precision classification. paper proposes multibranch models address this issue by effectively extracting spatial temporal features raw EEG data, where branches correspond different filter kernel sizes. The proposed method’s promising performance is demonstrated experimental results on two public datasets, BCI Competition IV 2a dataset High Gamma Dataset (HGD). technique show 9.61% improvement in accuracy EEGNet (MBEEGNet) fixed one-branch model, 2.95% variable model. In addition, ShallowConvNet (MBShallowConvNet) improved single-scale network 6.84%. outperformed other state-of-the-art methods.

Language: Английский

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A Novel QKD Approach to Enhance IIOT Privacy and Computational Knacks

Sensors, Journal Year: 2022, Volume and Issue: 22(18), P. 6741 - 6741

Published: Sept. 6, 2022

The industry-based internet of things (IIoT) describes how IIoT devices enhance and extend their capabilities for production amenities, security, efficacy. establishes an enterprise-to-enterprise setup that means industries have several factories manufacturing units are dependent on other sectors services products. In this context, individual need to share information with external in a shared environment which may not be secure. capability examine inspect such large-scale perform analytical protection over the large volumes personal organizational demands authentication confidentiality so total data endangered after illegal access by hackers unauthorized persons. parallel, these confidential industrial processed within reasonable time effective deliverables. Currently, there many mathematical-based symmetric asymmetric key cryptographic approaches identity- attribute-based public exist address abovementioned concerns limitations as computational overheads taking more crucial generation part encipherment decipherment process privacy security. addition, required generated third party compromised lead man-in-the-middle attacks, brute force etc. some quantum distribution available produce keys without party. However, still attacks photon number splitting faked state possible existing QKD approaches. primary motivation our work is avoid problems better optimal overhead generation, encipherment, compared conventional models. To overcome problems, we proposed novel dynamic (QKD) algorithm critical infrastructure, will secure all cyber-physical systems IIoT. paper, used multi-state qubit representation support enhanced dynamic, chaotic high efficiency low overhead. Our can create set qubits act session-wise encipher IIoT-based scales communication sensitive information.

Language: Английский

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