Research on MI EEG signal classification algorithm using multi-model fusion strategy coupling DOI
Quanyu Wu, Sheng Ding, Weige Tao

et al.

Computer Methods in Biomechanics & Biomedical Engineering, Journal Year: 2023, Volume and Issue: 28(1), P. 51 - 60

Published: Nov. 20, 2023

To enhance the accuracy of motor imagery(MI)EEG signal recognition, two methods, namely power spectral density and wavelet packet decomposition combined with a common spatial pattern, were employed to explore feature information in depth MI EEG signals. The extracted features subjected series fusion, F-test method was used select higher content. Here regarding classification, we further proposed Platt Scaling probability calibration calibrate results obtained from six basic classifiers, random forest (RF), support vector machines (SVM), Logistic Regression (LR), Gaussian naïve bayes (GNB), eXtreme Gradient Boosting (XGBoost), Light Machine (LightGBM). From these 12 three four selected for model fusion. validated on Datasets 2a 4th International BCI Competition, achieving an average data nine subjects reached 91.46%, which indicates that fusion effective improve classification accuracy, provides some reference value research brain-machine interface.

Language: Английский

Multi-scale spatiotemporal attention network for neuron based motor imagery EEG classification DOI
Venkata Chunduri, Yassine Aoudni, Samiullah Khan

et al.

Journal of Neuroscience Methods, Journal Year: 2024, Volume and Issue: 406, P. 110128 - 110128

Published: March 28, 2024

Language: Английский

Citations

7

EEG-based motor imagery classification with quantum algorithms DOI
Cynthia Olvera, Oscar Montiel, Yoshio Rubio

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 247, P. 123354 - 123354

Published: Jan. 30, 2024

Language: Английский

Citations

6

EISATC-Fusion: Inception Self-Attention Temporal Convolutional Network Fusion for Motor Imagery EEG Decoding DOI Creative Commons
Guangjin Liang, Dianguo Cao, Jinqiang Wang

et al.

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Journal Year: 2024, Volume and Issue: 32, P. 1535 - 1545

Published: Jan. 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)

Language: Английский

Citations

4

Deep learning in motor imagery EEG signal decoding: A Systematic Review DOI
Aurora Saibene, Hafez Ghaemi, Eda Dağdevır

et al.

Neurocomputing, Journal Year: 2024, Volume and Issue: 610, P. 128577 - 128577

Published: Sept. 14, 2024

Language: Английский

Citations

4

Deep Comparisons of Neural Networks from the EEGNet Family DOI Open Access
Csaba Márton Köllőd, András Adolf, Kristóf Iván

et al.

Electronics, Journal Year: 2023, Volume and Issue: 12(12), P. 2743 - 2743

Published: June 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.

Language: Английский

Citations

11

CET-attention mechanism impact on the classification of EEG signals DOI

Mouad Riyad,

Abdellah Adib

Annals of Telecommunications, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

Language: Английский

Citations

0

Parameter optimization of 3D convolutional neural network for dry-EEG motor imagery brain-machine interface DOI Creative Commons
Nobuaki Kobayashi,

M. Ino

Frontiers in Neuroscience, Journal Year: 2025, Volume and Issue: 19

Published: Feb. 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.

Language: Английский

Citations

0

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

Kamal Jeet Singh,

Nitin Singha,

Swati Bhalaik

et al.

The Journal of Supercomputing, Journal Year: 2025, Volume and Issue: 81(7)

Published: May 1, 2025

Language: Английский

Citations

0

Dual-pathway EEG model with channel attention for virtual reality motion sickness detection DOI
Chengcheng Hua,

Yuechi Chen,

Jianlong Tao

et al.

Journal of Neuroscience Methods, Journal Year: 2025, Volume and Issue: unknown, P. 110425 - 110425

Published: March 1, 2025

Language: Английский

Citations

0

Graph Convolutional Networks for Improved Motor Imagery Recognition in Brain-Computer Interfaces DOI
Vikas Raina, Renato R. Maaliw,

Kurbaniyazova Malohat Arislanbekovna

et al.

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 351 - 363

Published: Jan. 1, 2025

Language: Английский

Citations

0