Multi-scale spatiotemporal attention network for neuron based motor imagery EEG classification

Journal of Neuroscience Methods, Год журнала: 2024, Номер 406, С. 110128 - 110128

Опубликована: Март 28, 2024

Язык: Английский

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EEG-based motor imagery classification with quantum algorithms

Expert Systems with Applications, Год журнала: 2024, Номер 247, С. 123354 - 123354

Опубликована: Янв. 30, 2024

Язык: Английский

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Deep Comparisons of Neural Networks from the EEGNet Family

Electronics, Год журнала: 2023, Номер 12(12), С. 2743 - 2743

Опубликована: Июнь 20, 2023

A preponderance of brain–computer interface (BCI) publications proposing artificial neural networks for motor imagery (MI) electroencephalography (EEG) signal classification utilize one the BCI Competition datasets. However, these databases encompass MI EEG data from a limited number subjects, typically less than or equal to 10. Furthermore, algorithms usually include only bandpass filtering as means reducing noise and increasing quality. In this study, we conducted comparative analysis five renowned (Shallow ConvNet, Deep EEGNet, EEGNet Fusion, MI-EEGNet) utilizing open-access with larger subject pool in conjunction IV 2a dataset obtain statistically significant results. We employed FASTER algorithm eliminate artifacts processing step explored potential transfer learning enhance results on artifact-filtered data. Our objective was rank networks; hence, addition accuracy, introduced two supplementary metrics: accuracy improvement chance level effect learning. The former is applicable varying numbers classes, while latter can underscore robust generalization capabilities. metrics indicated that researchers should not disregard Shallow ConvNet they outperform later published members family.

Язык: Английский

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EISATC-Fusion: Inception Self-Attention Temporal Convolutional Network Fusion for Motor Imagery EEG Decoding

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Год журнала: 2024, Номер 32, С. 1535 - 1545

Опубликована: Янв. 1, 2024

The motor imagery brain-computer interface (MI-BCI) based on electroencephalography (EEG) is a widely used human-machine paradigm.However, due to the non-stationarity and individual differences among subjects in EEG signals, decoding accuracy limited, affecting application of MI-BCI.In this paper, we propose EISATC-Fusion model for MI decoding, consisting inception block, multi-head selfattention (MSA), temporal convolutional network (TCN), layer fusion.Specifically, design DS Inception block extract multi-scale frequency band information.And new cnnCosMSA module CNN cos attention solve collapse improve interpretability model.The TCN improved by depthwise separable convolution reduces parameters fusion consists feature decision fusion, fully utilizing features output enhances robustness model.We two-stage training strategy training.Early stopping prevent overfitting, loss validation set are as indicators early stopping.The proposed achieves within-subject classification accuracies 84.57% 87.58% BCI Competition IV Datasets 2a 2b, respectively.And cross-subject 67.42% 71.23% (by transfer learning) when with two sessions one session Dataset 2a, respectively.The demonstrated through weight visualization method.Index Terms-Brain-computer (BCI)

Язык: Английский

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Deep learning in motor imagery EEG signal decoding: A Systematic Review

Neurocomputing, Год журнала: 2024, Номер 610, С. 128577 - 128577

Опубликована: Сен. 14, 2024

Язык: Английский

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CET-attention mechanism impact on the classification of EEG signals

Annals of Telecommunications, Год журнала: 2025, Номер unknown

Опубликована: Фев. 17, 2025

Язык: Английский

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Parameter optimization of 3D convolutional neural network for dry-EEG motor imagery brain-machine interface

Frontiers in Neuroscience, Год журнала: 2025, Номер 19

Опубликована: Фев. 25, 2025

Easing the behavioral restrictions of those in need care not only improves their own quality life (QoL) but also reduces burden on workers and may help reduce number countries with declining birthrates. The brain-machine interface (BMI), which appliances machines are controlled by brain activity, can be used nursing settings to alleviate stress for care. It is expected workload workers. In this study, we focused motor imagery (MI) classification deep-learning construct a system that identify MI obtained electroencephalography (EEG) measurements high accuracy low latency response. By completing edge, privacy personal data ensured, ubiquitous, user convenience. On other hand, however, edge limited hardware resources, implementation models huge parameters computational cost, such as deep-learning, challenging. Therefore, optimizing measurement conditions various model, attempted power consumption improve response minimizing cost while maintaining accuracy. addition, investigated use 3-dimension convolutional neural network (3D CNN), retain spatial locality feature further We propose method maintain enabling processing size kernels layer structure. Furthermore, develop practical BMI system, introduced dry electrodes, more comfortable daily use, optimized memory proposed model even fewer less recall time, lower sampling rate. Compared EEGNet, 3D CNN parameters, multiply-accumulates, footprint approximately 75.9%, 16.3%, 12.5%, respectively, same level eight 3.5 seconds sample window size, 125 Hz rate 4-class dry-EEG MI.

Язык: Английский

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Dual-pathway EEG model with channel attention for virtual reality motion sickness detection

Journal of Neuroscience Methods, Год журнала: 2025, Номер unknown, С. 110425 - 110425

Опубликована: Март 1, 2025

Язык: Английский

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Graph Convolutional Networks for Improved Motor Imagery Recognition in Brain-Computer Interfaces

Communications in computer and information science, Год журнала: 2025, Номер unknown, С. 351 - 363

Опубликована: Янв. 1, 2025

Язык: Английский

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CCLNet: multiclass motor imagery EEG decoding through extended common spatial patterns and CNN-LSTM hybrid network

The Journal of Supercomputing, Год журнала: 2025, Номер 81(7)

Опубликована: Май 1, 2025

Язык: Английский

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