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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State-of-the-Art on Brain-Computer Interface Technology

Sensors, Journal Year: 2023, Volume and Issue: 23(13), P. 6001 - 6001

Published: June 28, 2023

This paper provides a comprehensive overview of the state-of-the-art in brain–computer interfaces (BCI). It begins by providing an introduction to BCIs, describing their main operation principles and most widely used platforms. The then examines various components BCI system, such as hardware, software, signal processing algorithms. Finally, it looks at current trends research related use for medical, educational, other purposes, well potential future applications this technology. concludes highlighting some key challenges that still need be addressed before widespread adoption can occur. By presenting up-to-date assessment technology, will provide valuable insight into where field is heading terms progress innovation.

Language: Английский

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Exploring Convolutional Neural Network Architectures for EEG Feature Extraction

Sensors, Journal Year: 2024, Volume and Issue: 24(3), P. 877 - 877

Published: Jan. 29, 2024

The main purpose of this paper is to provide information on how create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was understand the primary aspects creating and fine-tuning CNNs various application scenarios. We considered characteristics signals, coupled with an exploration signal processing data preparation techniques. These techniques include noise reduction, filtering, encoding, decoding, dimension among others. In addition, we conduct in-depth analysis well-known CNN architectures, categorizing them into four distinct groups: standard implementation, recurrent convolutional, decoder architecture, combined architecture. This further offers comprehensive evaluation these covering accuracy metrics, hyperparameters, appendix that contains table outlining parameters commonly used architectures feature extraction

Language: Английский

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Dynamic Convolution With Multilevel Attention for EEG-Based Motor Imagery Decoding

IEEE Internet of Things Journal, Journal Year: 2023, Volume and Issue: 10(21), P. 18579 - 18588

Published: June 1, 2023

Brain–computer interface (BCI) is an innovative technology that utilizes artificial intelligence (AI) and wearable electroencephalography (EEG) sensors to decode brain signals enhance the quality of life. EEG-based motor imagery (MI) signal used in many BCI applications, including smart healthcare, homes, robotics control. However, restricted ability a major factor preventing from expanding significantly. In this study, we introduce dynamic attention temporal convolutional network (D-ATCNet) for decoding MI signals. The D-ATCNet model uses convolution (Dy-conv) multilevel performance classification with relatively small number parameters. has two main blocks: 1) 2) convolution. Dy-conv encode low-level MI-EEG information shifted window self-attention extract high-level encoded signal. proposed performs better than existing methods accuracy 71.3% subject independent 87.08% dependent using competition IV-2a data set.

Language: Английский

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NF-EEG: A generalized CNN model for multi class EEG motor imagery classification without signal preprocessing for brain computer interfaces

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 92, P. 106081 - 106081

Published: Feb. 8, 2024

Language: Английский

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Spatial–temporal attention with graph and general neural network-based sign language recognition

Pattern Analysis and Applications, Journal Year: 2024, Volume and Issue: 27(2)

Published: April 4, 2024

Language: Английский

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Temporal–spatial transformer based motor imagery classification for BCI using independent component analysis

Biomedical Signal Processing and Control, Journal Year: 2023, Volume and Issue: 87, P. 105359 - 105359

Published: Aug. 25, 2023

Language: Английский

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Deep Learning for Enhanced Prosthetic Control: Real-Time Motor Intent Decoding for Simultaneous Control of Artificial Limbs

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Journal Year: 2024, Volume and Issue: 32, P. 1177 - 1186

Published: Jan. 1, 2024

The development of advanced prosthetic devices that can be seamlessly used during an individual's daily life remains a significant challenge in the field rehabilitation engineering. This study compares performance deep learning architectures to shallow networks decoding motor intent for control using electromyography (EMG) signals. Four neural network architectures, including feedforward with one hidden layer, multiple layers, temporal convolutional network, and squeeze-and-excitation operations were evaluated real-time, human-in-the-loop experiments able-bodied participants individual amputation. Our results demonstrate outperform intent, representation effectively extracting underlying information from EMG Furthermore, observed improvements by consistent across both amputee participants. By employing instead more reliable precise prosthesis achieved, which has potential significantly enhance functionality improve quality individuals amputations.

Language: Английский

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EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning

Journal of Neuroscience Methods, Journal Year: 2024, Volume and Issue: 409, P. 110215 - 110215

Published: July 3, 2024

Language: Английский

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Human Fall Detection Using 3D Multi-Stream Convolutional Neural Networks with Fusion

Diagnostics, Journal Year: 2022, Volume and Issue: 12(12), P. 3060 - 3060

Published: Dec. 6, 2022

Human falls, especially for elderly people, can cause serious injuries that might lead to permanent disability. Approximately 20-30% of the aged people in United States who experienced fall accidents suffer from head trauma, injuries, or bruises. Fall detection is becoming an important public healthcare problem. Timely and accurate incident could enable instant delivery medical services injured. New advances vision-based technologies, including deep learning, have shown significant results action recognition, where some focus on actions. In this paper, we propose automatic human system using multi-stream convolutional neural networks with fusion. The based a multi-level image-fusion approach every 16 frames input video highlight movement differences within range. This four consecutive preprocessed images are fed new proposed efficient lightweight CNN model four-branch architecture (4S-3DCNN) classifies whether there fall. evaluation included use more than 6392 generated sequences Le2i dataset, which publicly available dataset. method, three-fold cross-validation validate generalization susceptibility overfitting, achieved 99.03%, 99.00%, 99.68%, 99.00% accuracy, sensitivity, specificity, precision, respectively. experimental prove outperforms state-of-the-art models, GoogleNet, SqueezeNet, ResNet18, DarkNet19, detection.

Language: Английский

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