A Power Spectrum Pattern Difference-Based Time-Frequency Sub-Band Selection Method for MI-EEG Classification

Zheng, Lanhong; Ma, Yue; Lian, Pengchen; Yang, Xiao Li; Yi, Zhengkun; Song, Qiuzhi; Feng, Wei; Wang, Xinyu

doi:10.1109/jsen.2022.3171808

Cited by 12 publications

(5 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previously, researchers have extensively adopted traditional machine learning methods, typically categorized into two stages: feature extraction and feature classification. Frequently employed feature extraction algorithms include common spatial pattern, filter bank common spatial pattern (FBCSP), Fourier transform, power spectrum analysis, Wavelet transform, Autoregressive Model, and so forth [13][14][15][16][17]. Combining the features extracted by these algorithms with traditional classifiers such as linear discriminant analysis, support vector machines (SVM), and nearest neighbor classifiers has made essential contributions to decoding EEG signals [18,19].…”

Section: Introductionmentioning

confidence: 99%

Self-supervised contrastive learning for EEG-based cross-subject motor imagery recognition

Li,

Sun

et al. 2024

J. Neural Eng.

View full text Add to dashboard Cite

Objective. The extensive application of electroencephalography (EEG) in brain-computer interfaces (BCIs) can be attributed to its non-invasive nature and capability to offer high-resolution data. The acquisition of EEG signals is a straightforward process, but the datasets associated with these signals frequently exhibit data scarcity and require substantial resources for proper labeling. Furthermore, there is a significant limitation in the generalization performance of EEG models due to the substantial inter-individual variability observed in EEG signals. Approach. To address these issues, we propose a novel self-supervised contrastive learning framework for decoding motor imagery (MI) signals in cross-subject scenarios. Specifically, we design an encoder combining convolutional neural network and attention mechanism. In the contrastive learning training stage, the network undergoes training with the pretext task of data augmentation to minimize the distance between pairs of homologous transformations while simultaneously maximizing the distance between pairs of heterologous transformations. It enhances the amount of data utilized for training and improves the network’s ability to extract deep features from original signals without relying on the true labels of the data. Main results. To evaluate our framework’s efficacy, we conduct extensive experiments on three public MI datasets: BCI IV IIa, BCI IV IIb, and HGD datasets. The proposed method achieves cross-subject classification accuracies of 67.32%, 82.34%, and 81.13% on the three datasets, demonstrating superior performance compared to existing methods. Significance. Therefore, this method has great promise for improving the performance of cross-subject transfer learning in MI-based BCI systems.

show abstract

Section: Introductionmentioning

confidence: 99%

Self-supervised contrastive learning for EEG-based cross-subject motor imagery recognition

Li,

Sun

et al. 2024

J. Neural Eng.

View full text Add to dashboard Cite

show abstract

“…BCI technology has been widely used in rehabilitation engineering 3 , fatigue detection 4 , and smart home 5 . With the development of BCI technology, many typical paradigms have emerged, such as steady-state visually evoked potential (SSVEP) 6 , P300 7 , and motor imagery (MI) 8 . When the subject is stimulated by a specific frequency of vision, the visual cortex of the brain produces a continuous electrical response signal related to the stimulus frequency, which is called SSVEP 9 .…”

Section: Introductionmentioning

confidence: 99%

A fused multi-subfrequency bands and CBAM SSVEP-BCI classification method based on convolutional neural network

Lei,

Dong,

Guo

et al. 2024

Sci Rep

View full text Add to dashboard Cite

For the brain-computer interface (BCI) system based on steady-state visual evoked potential (SSVEP), it is difficult to obtain satisfactory classification performance for short-time window SSVEP signals by traditional methods. In this paper, a fused multi-subfrequency bands and convolutional block attention module (CBAM) classification method based on convolutional neural network (CBAM-CNN) is proposed for discerning SSVEP-BCI tasks. This method extracts multi-subfrequency bands SSVEP signals as the initial input of the network model, and then carries out feature fusion on all feature inputs. In addition, CBAM is embedded in both parts of the initial input and feature fusion for adaptive feature refinement. To verify the effectiveness of the proposed method, this study uses the datasets of Inner Mongolia University of Technology (IMUT) and Tsinghua University (THU) to evaluate the performance of the proposed method. The experimental results show that the highest accuracy of CBAM-CNN reaches 0.9813 percentage point (pp). Within 0.1–2 s time window, the accuracy of CBAM-CNN is 0.0201–0.5388 (pp) higher than that of CNN, CCA-CWT-SVM, CCA-SVM, CCA-GNB, FBCCA, and CCA. Especially in the short-time window range of 0.1–1 s, the performance advantage of CBAM-CNN is more significant. The maximum information transmission rate (ITR) of CBAM-CNN is 503.87 bit/min, which is 227.53 bit/min-503.41 bit/min higher than the above six EEG decoding methods. The study further results show that CBAM-CNN has potential application value in SSVEP decoding.

show abstract

“…Other limitations include a lack of specificity in distinguishing different MI tasks and high susceptibility to artifacts that distort the frequency pattern resulting in less accurate feature representation [11]. Nonetheless, both time and frequency domain features suffer from common problems, such as failing to capture spatial information and sensitivity to nonstationarities [12]. Hence, combined approaches of feature extraction where both temporal and frequency characteristics can be exploited are more commonly used, for example, event-related desynchronization/synchronization (ERD/ERS) [13], wavelet transform [14], and common spatial pattern (CSP) [15].…”

Section: Introductionmentioning

confidence: 99%

SCSP-3: A Spectrally Augmented Common Spatial Pattern Approach for Robust Motor Imagery-Based Brain–Computer Interface

Khanna,

Chowdhury,

Dutta

et al. 2024

IEEE Sensors J.

View full text Add to dashboard Cite

Common spatial pattern (CSP) is a widely used method for feature extraction in motor imagery (MI)-based brain-computer interface (BCI) development. However, the performance of traditional CSP features often lacks robustness against inter-session and inter-subject variabilities present in MI-related electroencephalogram (EEG) signals. To address this limitation, we propose a novel approach to CSP-based feature extraction, combining spectral information obtained from Welch power-spectrum (PS) estimation with temporal variations which we named here as SCSP-3. Our SCSP-3 method employs independent learning paths for the temporal and spectral features extracted through CSP. We introduce a postprocessing step that crosses the classification probabilities from these pathways using element-wise products, deriving linearly separable features. The performance of SCSP-3 is evaluated and compared to the traditional CSP approach utilizing a support vector machine (SVM) for classification following a within-subject evaluation scheme. The results demonstrate a significant improvement in average accuracy for SCSP-3 with more generalizability, as it performs equally well with datasets from healthy subjects and stroke patients. This enhanced robustness and generalizability highlight the potential of SCSP-3 as a superior alternative to traditional CSP-based feature extraction methods for achieving consistent performance across different subject categories.

show abstract

A Power Spectrum Pattern Difference-Based Time-Frequency Sub-Band Selection Method for MI-EEG Classification

Cited by 12 publications

References 55 publications

Self-supervised contrastive learning for EEG-based cross-subject motor imagery recognition

Self-supervised contrastive learning for EEG-based cross-subject motor imagery recognition

A fused multi-subfrequency bands and CBAM SSVEP-BCI classification method based on convolutional neural network

SCSP-3: A Spectrally Augmented Common Spatial Pattern Approach for Robust Motor Imagery-Based Brain–Computer Interface

Contact Info

Product

Resources

About