Journal of Northeastern University(Natural Science) ›› 2024, Vol. 45 ›› Issue (7): 928-935.DOI: 10.12068/j.issn.1005-3026.2024.07.003
Previous Articles Next Articles
Hong-li LI1(), Hao-yu LIU1, Rong-hua ZHANG2, Yi CHENG1
Received:
2023-03-05
Online:
2024-07-15
Published:
2024-10-29
Contact:
Hong-li LI
About author:
LI Hong-liE-mail:lihongliln@163.comCLC Number:
Hong-li LI, Hao-yu LIU, Rong-hua ZHANG, Yi CHENG. Emotional Classification Based on Multidimensional Feature Matrix and Improved Dense Connection Network[J]. Journal of Northeastern University(Natural Science), 2024, 45(7): 928-935.
名称 | 单位样本输入尺寸 | 单位样本输出尺寸 |
---|---|---|
输入 | (8,9,4) | (8,9,4) |
SE模块 | (8,9,4) | (8,9,4) |
卷积层(3×3) | (8,9,4) | (8,9,64) |
稠密连接层1 | (8,9,64) | (8,9,96) |
过渡层1 | (8,9,96) | (4,4,48) |
稠密连接层2 | (4,4,48) | (4,4,80) |
过渡层2 | (4,4,80) | (2,2,40) |
稠密连接层3 | (2,2,40) | (2,2,72) |
SE模块 | (2,2,72) | (2,2,72) |
全局池化层 | (2,2,72) | 72 |
全连接层 | 128 | 3 |
Table 1 Input parameter size of each layer of network
名称 | 单位样本输入尺寸 | 单位样本输出尺寸 |
---|---|---|
输入 | (8,9,4) | (8,9,4) |
SE模块 | (8,9,4) | (8,9,4) |
卷积层(3×3) | (8,9,4) | (8,9,64) |
稠密连接层1 | (8,9,64) | (8,9,96) |
过渡层1 | (8,9,96) | (4,4,48) |
稠密连接层2 | (4,4,48) | (4,4,80) |
过渡层2 | (4,4,80) | (2,2,40) |
稠密连接层3 | (2,2,40) | (2,2,72) |
SE模块 | (2,2,72) | (2,2,72) |
全局池化层 | (2,2,72) | 72 |
全连接层 | 128 | 3 |
模型 | 正确率 |
---|---|
BiLSTM[ | 90.22 |
DCRN[ | 91.18 |
DAEN[ | 91.01 |
STRNN[ | 89.50 |
SVM, LOSO[ | 83.33 |
MCNN [ | 91.31 |
GELM [ | 91.07 |
DGCNN [ | 90.40 |
本文模型 | 96.03 |
Table 2 Classification accuracy of different models
模型 | 正确率 |
---|---|
BiLSTM[ | 90.22 |
DCRN[ | 91.18 |
DAEN[ | 91.01 |
STRNN[ | 89.50 |
SVM, LOSO[ | 83.33 |
MCNN [ | 91.31 |
GELM [ | 91.07 |
DGCNN [ | 90.40 |
本文模型 | 96.03 |
9通道 | 15通道 | 33通道 | 62通道 | |
---|---|---|---|---|
1 | 74.48 | 87.75 | 92.32 | 93.48 |
2 | 76.09 | 92.14 | 94.75 | 97.33 |
3 | 84.89 | 90.03 | 93.24 | 96.45 |
4 | 86.07 | 97.79 | 99.86 | 99.85 |
5 | 76.67 | 86.54 | 94.57 | 95.41 |
6 | 88.64 | 90.77 | 98.53 | 99.44 |
7 | 80.24 | 86.23 | 93.32 | 92.46 |
8 | 86.45 | 91.64 | 96.54 | 97.33 |
9 | 84.85 | 89.31 | 97.35 | 96.59 |
10 | 86.75 | 91.25 | 97.37 | 97.34 |
11 | 83.43 | 89.42 | 93.36 | 95.56 |
12 | 81.56 | 88.64 | 92.41 | 95.71 |
13 | 83.16 | 89.72 | 96.32 | 95.27 |
14 | 82.48 | 89.93 | 97.78 | 96.44 |
15 | 75.69 | 85.56 | 90.03 | 91.86 |
平均 | 82.10 | 89.78 | 95.18 | 96.03 |
Table 3 Classification accuracy of each subject on different number of electrode channels
9通道 | 15通道 | 33通道 | 62通道 | |
---|---|---|---|---|
1 | 74.48 | 87.75 | 92.32 | 93.48 |
2 | 76.09 | 92.14 | 94.75 | 97.33 |
3 | 84.89 | 90.03 | 93.24 | 96.45 |
4 | 86.07 | 97.79 | 99.86 | 99.85 |
5 | 76.67 | 86.54 | 94.57 | 95.41 |
6 | 88.64 | 90.77 | 98.53 | 99.44 |
7 | 80.24 | 86.23 | 93.32 | 92.46 |
8 | 86.45 | 91.64 | 96.54 | 97.33 |
9 | 84.85 | 89.31 | 97.35 | 96.59 |
10 | 86.75 | 91.25 | 97.37 | 97.34 |
11 | 83.43 | 89.42 | 93.36 | 95.56 |
12 | 81.56 | 88.64 | 92.41 | 95.71 |
13 | 83.16 | 89.72 | 96.32 | 95.27 |
14 | 82.48 | 89.93 | 97.78 | 96.44 |
15 | 75.69 | 85.56 | 90.03 | 91.86 |
平均 | 82.10 | 89.78 | 95.18 | 96.03 |
1 | 郑伟龙,石振锋,吕宝粮.用异质迁移学习构建跨被试脑电情感模型[J].计算机学报,2020,43(2):177-189. |
Zheng Wei‑long, Shi Zhen‑feng, Bao‑liang Lyu.Building cross‑subject EEG‑based affective models using heterogeneous transfer learning[J].Chinese Journal of Computers,2020,43(2):177-189. | |
2 | 权学良,曾志刚,蒋建华,等.基于生理信号的情感计算研究综述[J].自动化学报,2021,47(8):1769-1784. |
Quan Xue‑liang, Zeng Zhi‑gang, Jiang Jian‑hua,et al.Physiological signals based affective computing:a systematic review[J].Acta Automatica Sinica,2021,47(8):1769-1784. | |
3 | Sani O G, Yang Y X, Lee M B,et al.Mood variations decoded from multi‑site intracranial human brain activity[J].Nature Biotechnology,2018,36(10):954-961. |
4 | Zheng W L, Liu W, Lu Y F,et al.EmotionMeter:a multimodal framework for recognizing human emotions[J].IEEE Transactions on Cybernetics,2019,49(3):1110-1122. |
5 | 褚亚奇,朱波,赵新刚,等.基于时空特征学习卷积神经网络的运动想象脑电解码方法[J].生物医学工程学杂志,2021,38(1):1-9. |
Chu Ya‑qi, Zhu Bo, Zhao Xin‑gang,et al.Convolutional neural network based on temporal‑spatial feature learning for motor imagery electroencephalogram signal decoding[J]. Journal of Biomedical Engineering,2021,38(1):1-9. | |
6 | Fdez J, Guttenberg N, Witkowski O,et al.Cross‑subject EEG‑based emotion recognition through neural networks with stratified normalization[J].Frontiers in Neuroscience,2021,15:626277. |
7 | Joshi V M, Ghongade R B.EEG based emotion detection using fourth order spectral moment and deep learning[J].Biomedical Signal Processing and Control,2021,68:102755.1-102755.12. |
8 | 周如双,赵慧琳,林玮玥,等.基于深浅特征融合的深度卷积残差网络的脑电情绪识别模型[J].中国生物医学工程学报,2021,40(6):641-652. |
Zhou Ru‑shuang, Zhao Hui‑lin, Lin Wei‑yue,et al.Feature fusion based deep residual networks using deep and shallow learning for EEG‑based emotion recognition[J].Chinese Journal of Biomedical Engineering,2021,40(6):641-652. | |
9 | Liu W, Zheng W L, Lu B L.Emotion recognition using multimodal deep learning[C]//International Conference on Neural Information Processing.Cham:Springer,2016:521-529. |
10 | Zhang T, Zheng W, Cui Z,et al.Spatial‑temporal recurrent neural network for emotion recognition[J].IEEE Transactions on Cybernetics,2017:839-847. |
11 | Song D W, Zhang Y Z, Hu B,et al.Exploring EEG features in cross‑subject emotion recognition[J].Frontiers in Neuroscience,2018,12:162. |
12 | Duan R N, Zhu J Y, Lu B L.Differential entropy feature for EEG‑based emotion classification[C]//2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).San Diego,2013:1-4. |
13 | 苗敏敏,徐宝国,胡文军,等.基于自适应优化空频微分熵的情感脑电识别[J].仪器仪表学报,2021,42(3):221-230. |
Miao Min‑min, Xu Bao‑guo, Hu Wen‑jun,et al.Emotion EEG recognition based on the adaptive optimized spatial‑frequency differential entropy[J].Chinese Journal of Scientific Instrument,2021,42(3):221-230. | |
14 | Zheng W L, Lu B L.Investigating critical frequency bands and channels for EEG‑based emotion recognition with deep neural networks[J].IEEE Transactions on Autonomous Mental Development,2015,7(3):162-175. |
15 | Shi L C, Jiao Y Y, Lu B L.Differential entropy feature for EEG‑based vigilance estimation[C]//International Conference of the IEEE Engineering in Medicine & Biology Society. San Diego,2013:6627-6630. |
16 | 戴紫玉,马玉良,高云园,等.基于多尺度卷积核CNN的脑电情绪识别[J].传感技术学报,2021,34(4):496-503. |
Dai Zi‑yu, Ma Yu‑liang, Gao Yun‑yuan,et al.A multi‑scale convolutional kernel CNN for EEG emotion recognition[J].Journal of Transduction Technology 2021,34(4):496-503. | |
17 | 李红利,尹飞超,张荣华,等.基于通道注意力和稀疏时频分解的运动想象分类[J].生物医学工程学杂志,2022,39(3):488-497. |
Li Hong‑li, Yin Fei‑chao, Zhang Rong‑hua,et al.Motor imagery electroencephalogram classification based on sparse spatiotemporal decomposition and channel attention[J].Journal of Biomedical Engineering,2022,39(3):488-497. | |
18 | Hu J, Shen L, Sun G.Squeeze‑and‑excitation networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City,2018:7132-7141. |
19 | 晁浩,曹益鸣,刘永利.基于三维特征矩阵和冲压激励网络的多通道脑电情感识别[J].控制与决策,2023,38(12):3427-3435. |
Chao Hao, Cao Yi‑ming, Liu Yong‑li.Emotion recognition from multi‑channel EEG data through three‑dimensional feature matrix and squeeze‑and‑excitation networks[J].Control and Decision,2023,38(12):3427-3435. | |
20 | Chen B L, Zhao T S, Liu J H,et al.Multipath feature recalibration DenseNet for image classification[J].International Journal of Machine Learning and Cybernetics,2021,12(3):651-660. |
21 | 柴国强,王大为,芦宾,等.基于注意机制的轻量化稠密连接网络单幅图像去雨[J].北京航空航天大学学报,2022,48(11):2186-2192. |
Chai Guo‑qiang, Wang Da‑wei, Lu Bin,et al.Lightweight dense network based on attention mechanism for single‑image deraining[J].Journal of Beijing University of Aeronautics and Astronautics,2022,48(11):2186-2192. | |
22 | Kingma D P, Ba J.Adam:a method for stochastic optimization[C]// The 3rd International Conference on Learning Representations.San Diego,2015:1412.6980. |
23 | Dang W D, Lyu D M, Li R M,et al.Multilayer network‑based CNN model for emotion recognition[J].International Journal of Bifurcation and Chaos in Applied Sciences and Engineering,2022,32(1):2250011. |
24 | Zheng W L, Zhu J Y, Lu B L.Identifying stable patterns over time for emotion recognition from EEG[J].IEEE Transactions on Affective Computing,2019,10(3):417-429. |
25 | Song T, Zheng W, Song P,et al.EEG emotion recognition using dynamical graph convolutional neural networks[J].IEEE Transactions on Affective Computing,2020,11(3):532-541. |
26 | Zheng W L, Guo H T, Lu B L.Revealing critical channels and frequency bands for emotion recognition from EEG with deep belief network[C]//7th International IEEE/EMBS Conference on Neural Engineering (NER).Montpellier,2015:154-157. |
[1] | Ji-hong LIU, Lü-heng ZHANG, Hai-xu YANG. A Saturation Artifact Inpainting Algorithm for Cell Fluorescence Microscopic Images [J]. Journal of Northeastern University(Natural Science), 2024, 45(7): 921-927. |
[2] | Dong-hong HAN, Yan-ru KONG, Yi-meng ZHAN, Yuan LIU. Research on Emotion Recognition Method of Music Multimodal Data [J]. Journal of Northeastern University(Natural Science), 2024, 45(6): 776-785. |
[3] | Wei-qi ZHANG, Hui-ming WANG. Interpretable Deep Learning Prediction Model for Compressive Strength of Concrete [J]. Journal of Northeastern University(Natural Science), 2024, 45(5): 738-744. |
[4] | WEI Jian-yi, WU Jing-jing. Resource Allocation Algorithm in Industrial Internet of Things Based on Edge Computing [J]. Journal of Northeastern University(Natural Science), 2023, 44(8): 1072-1078. |
[5] | YANG Xuan, HE Zhan-qi. Improved Two-layer BiLSTM Electrocardiosignal Segmentation Method [J]. Journal of Northeastern University(Natural Science), 2023, 44(12): 1705-1711. |
[6] | JI Ce, WANG Xin, GENG Rong, LIANG Min-jun. LSTM-Based Channel Estimation Method in Time-Varying Channels [J]. Journal of Northeastern University(Natural Science), 2023, 44(11): 1521-1528. |
[7] | CHEN Cheng, SHI Pei-xin, WANG Zhan-sheng, JIA Peng-jiao. Shield Load Prediction Method Based on Deep Learning with Multiattention Mechanism [J]. Journal of Northeastern University(Natural Science), 2023, 44(11): 1631-1638. |
[8] | LI Juan-li, WEI Dai-liang, LI Bo, WEN Xiao. Improved SSD Rapid Separation Model of Coal Gangue Based on Deep Learning and Light-Weighting [J]. Journal of Northeastern University(Natural Science), 2023, 44(10): 1474-1480. |
[9] | ZHAO Yong, JIAO Shi-hui, ZHAO Qian-bai. Hybrid Recognition Model of Microseismic Signals for Mining Based on Mel Spectrum and LSTM-DCNN [J]. Journal of Northeastern University(Natural Science), 2023, 44(10): 1481-1489. |
[10] | ZHANG Xue-feng, WANG Zhao-yi. Automatic Lane Change Decision Model Based on Dueling Double Deep Q-network [J]. Journal of Northeastern University(Natural Science), 2023, 44(10): 1369-1376. |
[11] | WANG Ying, WANG Ze-hao, LI Hong, HUANG Wen-jun. Named Entity Recognition in Threat Intelligence Domain Based on Deep Learning [J]. Journal of Northeastern University(Natural Science), 2023, 44(1): 33-39. |
[12] | GU De-ying, LUO Yu-lun, LI Wen-chao. Traffic Target Detection in Complex Scenes Based on Improved YOLOv5 Algorithm [J]. Journal of Northeastern University(Natural Science), 2022, 43(8): 1073-1079. |
[13] | DAI Yin, LIU Wei-bin, DONG Xin-yang, SONG Yu-meng. U-Net CSF Cells Segmentation Based on Attention Mechanism [J]. Journal of Northeastern University(Natural Science), 2022, 43(7): 944-950. |
[14] | LI Hong-ru, REN Zi-yang, HUANG You-he, YU Xia. Recognition Method of Arrhythmia Based on Variable Weight Singular Spectrum Analysis [J]. Journal of Northeastern University(Natural Science), 2022, 43(3): 305-312. |
[15] | GU De-ying, ZHANG Song, MENG Fan-wei. Vehicle 3D Space Detection Method Based on Monocular Vision [J]. Journal of Northeastern University(Natural Science), 2022, 43(3): 328-334. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||