A Model Combining Multi Branch Spectral-Temporal CNN, Efficient Channel Attention, and LightGBM for MI-BCI Classification

Jia, Hai; Yu, Shiqi; Yin, Shunjie; Liu, Lanxin; Yi, Chanlin; Xue, Kaiqing; Li, Fali; Yao, Dezhong; Xu, Peng; Zhang, Tao

doi:10.1109/tnsre.2023.3243992

Cited by 33 publications

(17 citation statements)

References 58 publications

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“…In contrast, four-class classification with time-frequency images using the same dataset yielded an average accuracy of 80.7% 25 and 71.25%. 26 CNN classification methods using nonimage representation of EEG also yielded lower average accuracy of 83%, 23 79.01%, 28 74%, 29 and 74.26% 27 in comparison to the proposed method. Additionally, our method yields an average accuracy of 93.99% for two-class classification, whereas a method using time-frequency images yields an average accuracy of 85.59% 11 with the same dataset.…”

Section: Discussionmentioning

confidence: 87%

Use of covariance matrix images for electroencephalography signal classification for multiclass motor imagery‐based brain computer interface

Thrikkannoor Kolathod,

Sanjay

2023

Int J Imaging Syst Tech

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Electroencephalogram (EEG) signals can be used to control devices using brain computer interface (BCI) systems, which offer assistance to patients with neuromuscular diseases. This article aims to determine the feasibility of using spatial covariance matrices with deep convolutional neural networks for multiclass motor imagery (MI) task classification for EEG‐based BCIs. Covariance matrices derived from multichannel EEG data are converted into color‐scaled images, which are used to train the AlexNet neural network using transfer learning. Samples of 2‐s duration are extracted every 200 ms intervals from each MI activity EEG eventually increasing the training data size sixfold and reducing the latency in real‐time implementations. Observed the performance using two popular publicly available BCI datasets for two‐class and four‐class MI classification. BCI competition IV Dataset IIa has shown average accuracy of 93.99% (k = 0.88) for two‐class and 86.70% (k = 0.82) for four‐class MI classification. BCI competition III Dataset IVa exhibited average accuracy of 97.02% (k = 0.94) for two‐class classification. Good performance was observed for training with cross‐subject data for two‐class and four‐class classification. The proposed method has shown improved accuracy for multiclass MI classification compared with existing works. The CNN is able to extract features from the covariance matrix and classify the MI data with reasonable accuracy. Since this method can handle large‐dimension data, dimension reduction, and source separation techniques are not required.

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Section: Discussionmentioning

confidence: 87%

Use of covariance matrix images for electroencephalography signal classification for multiclass motor imagery‐based brain computer interface

Thrikkannoor Kolathod,

Sanjay

2023

Int J Imaging Syst Tech

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“…In this thesis, the channel attention ECA module with a better attention effect is proposed in ECA-Net [35] (Efficient Channel Attention Networks) by Tianjin University and is referenced. ECA, which is simple to implement and has outstanding effects, has been widely used in many studies [36][37][38][39], but its further research is very few. This paper proposes C-ECA based on the work of ECA.…”

Section: Attention Mechanismmentioning

confidence: 99%

Improved Light-Weight Target Detection Method Based on YOLOv5

Shi

Zhu

Su³

2023

IEEE Access

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Aiming at the problems of the YOLOv5, such as large model size, enormous amount of computation, and low accuracy of target box regression, a new model with fewer parameters, less computation, faster convergence speed, and higher accuracy was proposed. Firstly, the improved SKConv was used to greatly reduce the number of model parameters and increase the receptive field range of the network. Secondly, C-ECA, an optimized version of the channel attention module ECA, was added to the model to obtain attention information in a cross-channel way, so that the model could more accurately focus on important features among complex features. Then, the MSM structure is designed to effectively improve the feature extraction ability of the network. Finally, the backbone network is deepened to extract more intermediate features, and the depth of the feature pyramid and detection layer are increased accordingly so that the network can make full use of intermediate features and accurately detect more targets. The experimental results show that compared with YOLOv5s, the number of parameters of the final models is reduced by 18.6%, and the calculation amount is reduced by 8.1%, and when tested on the VOC2007 test dataset,

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“…At present, extensive use of deep learning-based frameworks has been widely popular for analyzing biomedical data analysis [12][13][14]. In [15], a feature extraction scheme based on tunable Q-factor wavelet transform (TQWT) is proposed, where TQWT-feature block sequences are finally applied to the proposed hybrid convolutional recurrent neural network (HCRNN).…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, another advantage of the proposed CWT-based method is the relevant EEG channel selection technique which can reduce the computational burden, data storage and the discomfort of wearable EEG devices. As the CNN based models in [12,13] are effective for extracting more discriminative features from biomedical data, a lightweight CNN architecture utilizing the proposed CEF2D feature matrix has proved to be very effective in categorizing emotions.…”

Section: Introductionmentioning

confidence: 99%

Emotion recognition with reduced channels using CWT based EEG feature representation and a CNN classifier

Mahmud,

Fattah,

Saquib

et al. 2024

Biomed. Phys. Eng. Express

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Objective. Although emotion recognition has been studied for decades, a more accurate classification method that requires less computing is still needed. At present, in many studies, EEG features are extracted from all channels to recognize emotional states, however, there is a lack of an efficient feature domain that improves classification performance and reduces the number of EEG channels. Approach. In this study, a continuous wavelet transform (CWT)-based feature representation of multi-channel EEG data is proposed for automatic emotion recognition. In the proposed feature, the time-frequency domain information is preserved by using CWT coefficients. For a particular EEG channel, each CWT coefficient is mapped into a strength-to-entropy component ratio to obtain a 2D representation. Finally, a 2D feature matrix, namely CEF2D, is created by concatenating these representations from different channels and fed into a deep convolutional neural network architecture. Based on the CWT domain energy-to-entropy ratio, effective channel and CWT scale selection schemes are also proposed to reduce computational complexity. Main results. Compared with previous studies, the results of this study show that valence and arousal classification accuracy has improved in both 3-class and 2-class cases. For the 2-class problem, the average accuracies obtained for valence and arousal dimensions are 98.83% and 98.95%, respectively, and for the 3-class, the accuracies are 98.25% and 98.68%, respectively. Significance. Our findings show that the entropy-based feature of EEG data in the CWT domain is effective for emotion recognition. Utilizing the proposed feature domain, an effective channel selection method can reduce computational complexity.

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A Model Combining Multi Branch Spectral-Temporal CNN, Efficient Channel Attention, and LightGBM for MI-BCI Classification

Cited by 33 publications

References 58 publications

Use of covariance matrix images for electroencephalography signal classification for multiclass motor imagery‐based brain computer interface

Use of covariance matrix images for electroencephalography signal classification for multiclass motor imagery‐based brain computer interface

Improved Light-Weight Target Detection Method Based on YOLOv5

Emotion recognition with reduced channels using CWT based EEG feature representation and a CNN classifier

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