EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Journal Year: 2022, Volume and Issue: 31, P. 710 - 719

Published: Dec. 16, 2022

Due to the limited perceptual field, convolutional neural networks (CNN) only extract local temporal features and may fail capture long-term dependencies for EEG decoding. In this paper, we propose a compact Convolutional Transformer, named Conformer, encapsulate global in unified classification framework. Specifically, convolution module learns low-level throughout one-dimensional spatial layers. The self-attention is straightforwardly connected correlation within features. Subsequently, simple classifier based on fully-connected layers followed predict categories signals. To enhance interpretability, also devise visualization strategy project class activation mapping onto brain topography. Finally, have conducted extensive experiments evaluate our method three public datasets EEG-based motor imagery emotion recognition paradigms. experimental results show that achieves state-of-the-art performance has great potential be new baseline general code been released https://github.com/eeyhsong/EEG-Conformer.

Language: Английский

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DenseSPH-YOLOv5: An automated damage detection model based on DenseNet and Swin-Transformer prediction head-enabled YOLOv5 with attention mechanism

Advanced Engineering Informatics, Journal Year: 2023, Volume and Issue: 56, P. 102007 - 102007

Published: April 1, 2023

Language: Английский

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WilDect-YOLO: An efficient and robust computer vision-based accurate object localization model for automated endangered wildlife detection

Ecological Informatics, Journal Year: 2022, Volume and Issue: 75, P. 101919 - 101919

Published: Nov. 18, 2022

Language: Английский

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Real-time growth stage detection model for high degree of occultation using DenseNet-fused YOLOv4

Computers and Electronics in Agriculture, Journal Year: 2022, Volume and Issue: 193, P. 106694 - 106694

Published: Jan. 17, 2022

Language: Английский

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Adaptive transfer learning-based multiscale feature fused deep convolutional neural network for EEG MI multiclassification in brain–computer interface

Engineering Applications of Artificial Intelligence, Journal Year: 2022, Volume and Issue: 116, P. 105347 - 105347

Published: Aug. 30, 2022

Language: Английский

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Deep Learning-Based Cost-Effective and Responsive Robot for Autism Treatment

Drones, Journal Year: 2023, Volume and Issue: 7(2), P. 81 - 81

Published: Jan. 23, 2023

Recent studies state that, for a person with autism spectrum disorder, learning and improvement is often seen in environments where technological tools are involved. A robot an excellent tool to be used therapy teaching. It can transform teaching methods, not just the classrooms but also in-house clinical practices. With rapid advancement deep techniques, robots became more capable of handling human behaviour. In this paper, we present cost-efficient, socially designed called ‘Tinku’, developed assist special needs children. ‘Tinku’ low cost full features has ability produce human-like expressions. Its design inspired by widely accepted animated character ‘WALL-E’. capabilities include offline speech processing computer vision—we light object detection models, such as Yolo v3-tiny single shot detector (SSD)—for obstacle avoidance, non-verbal communication, expressing emotions anthropomorphic way, etc. uses onboard technique localize objects scene information semantic perception. We have several lessons training using these features. sample lesson about brushing discussed show robot’s capabilities. Tinku cute, loaded lots features, management all processes mind-blowing. supervision experts its condition application taken care of. small survey on appearance discussed. More importantly, it tested children acceptance technology compatibility terms voice interaction. helps autistic kids state-of-the-art models. Autism Spectral disorders being increasingly identified today’s world. The that prone interact comfortably than instructor. To fulfil demand, presented cost-effective solution form some common autism-affected child.

Language: Английский

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An efficient and robust Phonocardiography (PCG)-based Valvular Heart Diseases (VHD) detection framework using Vision Transformer (ViT)

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 158, P. 106734 - 106734

Published: March 1, 2023

Language: Английский

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SQL and NoSQL Database Software Architecture Performance Analysis and Assessments—A Systematic Literature Review

Big Data and Cognitive Computing, Journal Year: 2023, Volume and Issue: 7(2), P. 97 - 97

Published: May 12, 2023

The competent software architecture plays a crucial role in the difficult task of big data processing for SQL and NoSQL databases. databases were created to organize allow horizontal expansion. databases, on other hand, support scalability can efficiently process large amounts unstructured data. Organizational needs determine which paradigm is appropriate, yet selecting best option not always easy. Differences database design are what set apart. Each type also consistently employs mixed-model approach. Therefore, it challenging cloud users transfer their among different storage services (CSPs). There several paradigms being monitored by various platforms (IaaS, PaaS, SaaS, DBaaS). purpose this SLR examine articles that address portability interoperability, as well architectures Numerous studies comparing capabilities particularly Oracle RDBMS Document Database (MongoDB), terms scale, performance, availability, consistency, sharding, presented part state art. Research indicates with specifically tailored structures, may be analytics, while suited online transaction (OLTP) purposes.

Language: Английский

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Deep learning-accelerated computational framework based on Physics Informed Neural Network for the solution of linear elasticity

Neural Networks, Journal Year: 2023, Volume and Issue: 162, P. 472 - 489

Published: March 13, 2023

Language: Английский

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Cognitive neuroscience and robotics: Advancements and future research directions

Robotics and Computer-Integrated Manufacturing, Journal Year: 2023, Volume and Issue: 85, P. 102610 - 102610

Published: July 24, 2023

In recent years, brain-based technologies that capitalise on human abilities to facilitate human–system/robot interactions have been actively explored, especially in brain robotics. Brain–computer interfaces, as applications of this conception, set a path convert neural activities recorded by sensors from the scalp via electroencephalography into valid commands for robot control and task execution. Thanks advancement sensor technologies, non-invasive invasive headsets designed developed achieve stable recording brainwave signals. However, robust accurate extraction interpretation signals robotics are critical reliable task-oriented opportunistic such brainwave-controlled robotic interactions. response need, pervasive advanced analytical approaches translating merging functions, behaviours, tasks, environmental information focus brain-controlled applications. These methods composed signal processing, feature extraction, representation activities, command conversion control. Artificial intelligence algorithms, deep learning, used classification, recognition, identification patterns intent underlying brainwaves form electroencephalography. Within context, paper provides comprehensive review past current status at intersection robotics, neuroscience, artificial highlights future research directions.

Language: Английский

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