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Beyond Static Representations: Learning Electroencephalogram Spatiotemporal Dynamics for Enhanced Emotion Recognition.

Code ↔ Paper

9 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 9 matches
  1. [1] § Methods › Experiments › Implementation details ↔ Train.py, lines 44 to 91 · score 0.69 · RMSprop, weight decay, alpha, optimizer, epochs, trained · Report a mismatch
  2. [2] § Results › Decoding accuracies ↔ Train.py, lines 229 to 255 · score 0.66 · LGGNet, TSception, DSE TAM, Deformer, DGCNN, Conformer · Report a mismatch
  3. [3] § Results › Decoding accuracies ↔ ChooseNet.py, lines 10 to 76 · score 0.62 · LGGNet, TSception, DSE TAM, Deformer, DGCNN, Conformer · Report a mismatch
  4. [4] § Methods › [CLS] token classifier ↔ Train.py, lines 44 to 91 · score 0.60 · cross entropy, loss function, predicted, train · Report a mismatch
  5. [5] § Results › Decoding accuracies ↔ ChooseNet.py, lines 10 to 76 · score 0.58 · LGGNet, TSception, DSE TAM, Deformer, DGCNN, Conformer · Report a mismatch
  6. [6] § Results › Decoding accuracies ↔ Train.py, lines 229 to 255 · score 0.57 · TSception, DSE TAM, LOSO, DGCNN, Conformer, ACRNN · Report a mismatch
  7. [7] § Methods ↔ net/DSE_TAM.py, lines 231 to 264 · score 0.55 · pre STM, DSE TAM, classifier, CLS, embedding, layer · Report a mismatch
  8. [8] § Methods › Dynamic Spatial Embedding ↔ net/LGGNet.py, lines 56 to 182 · score 0.52 · convolutional kernel, power, aggregated, filtered, stride, batch · Report a mismatch
  9. [9] § Methods › Experiments › Implementation details ↔ net/DGCNN.py, lines 89 to 129 · score 0.51 · graph convolutional layers, EEG signals, dimension, channels, module · Report a mismatch

Paper

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The authors' code

Python · 272 lines · 10 KB · no license · 4 matches

  1. from torch import nn
  2. from Dataset.LOSO import *
  3. from Dataset.KFold import *
  4. from ChooseNet import *
  5. from utils import *
  6. from sklearn.metrics import confusion_matrix
  8. def evaluate_model(model, dataloader, lossFunction, device):
  9. model.eval() # 将模型设为评估模式
  10. running_loss = 0.0
  11. correct_preds = 0
  12. total_preds = 0
  13. all_labels = []
  14. all_predictions = []
  16. with torch.no_grad():
  17. for inputs, labels in dataloader:
  18. inputs, labels = inputs.to(device), labels.to(device)
  19. # 前向传播
  20. outputs = model(inputs)
  21. # 计算损失
  22. loss = lossFunction(outputs, labels)
  23. # 统计损失
  24. running_loss += loss.item() * inputs.size(0)
  25. # 统计正确预测的样本数量
  26. _, predicted = torch.max(outputs, 1)
  27. correct_preds += (predicted == labels).sum().item()
  28. total_preds += labels.size(0)
  30. # 保存真实标签和预测标签
  31. all_labels.extend(labels.cpu().numpy())
  32. all_predictions.extend(predicted.cpu().numpy())
  34. # 计算平均损失和准确率
  35. avg_loss = running_loss / len(dataloader.dataset)
  36. accuracy = correct_preds / total_preds
  38. labels_range = list(range(3))
  39. cm = confusion_matrix(all_labels, all_predictions, labels=labels_range)
  41. return avg_loss, accuracy, cm
  44. def train_and_validation(net, train_iter, test_iter, num_epochs, lr, weight_decay, device):
  45. lossFunction = nn.CrossEntropyLoss()
  46. optimizer = torch.optim.RMSprop(net.parameters(), lr=lr, weight_decay=weight_decay, alpha=0.99)
  47. net.to(device)
  48. new_lr = None
  49. state = None
  50. max_acc = 0.0
  51. train_losses, val_losses, val_accs = [], [], [] # 用于存储每个 epoch 的训练损失
  52. val_cm = []
  55. for epoch in range(num_epochs):
  56. net.train() # 将模型设为训练模式
  57. running_loss = 0.0
  58. correct_preds = 0
  59. total_preds = 0
  60. for inputs, labels in train_iter:
  61. inputs, labels = inputs.to(device), labels.to(device)
  62. # inputs = inputs.reshape(32, 5, 62, 5)
  63. # labels = labels.reshape(32, 5)
  64. # 前向传播
  65. outputs = net(inputs)
  66. # 计算损失
  67. loss = lossFunction(outputs, labels)
  68. # 反向传播和优化
  69. optimizer.zero_grad()
  70. loss.backward()
  71. optimizer.step()
  72. # 统计损失
  73. running_loss += loss.item() * inputs.size(0)
  74. # 统计正确预测的样本数量
  75. _, predicted = torch.max(outputs, 1)
  76. correct_preds += (predicted == labels).sum().item()
  77. total_preds += labels.size(0)
  79. epoch_acc = correct_preds / total_preds
  80. epoch_loss = running_loss / len(train_iter.dataset)
  81. print(f'Train Epoch [{epoch + 1}/{num_epochs}], Loss: {epoch_loss:.4f}, Accuracy: {epoch_acc:.4f}')
  82. val_loss, val_acc, cm = evaluate_model(net, test_iter, lossFunction, device)
  83. print(f'Validation Epoch [{epoch + 1}/{num_epochs}], Loss: {val_loss:.4f}, Accuracy: {val_acc:.4f}')
  85. train_losses.append(epoch_loss)
  86. val_losses.append(val_loss)
  87. val_accs.append(val_acc)
  88. val_cm.append(cm)
  90. val_cm = np.array(val_cm)
  91. return train_losses, val_losses, val_accs, val_cm
  94. def train_by_LOSO(params):
  95. torch.autograd.set_detect_anomaly(True)
  96. sub_train_loss, sub_val_loss, sub_val_acc = [], [], []
  97. sub_cm = []
  99. subjects = params['subjects']
  100. print(f"Starting LOSO training for {subjects} subjects.")
  101. for i in range(1, subjects + 1):
  102. print(f"Training for Subject {i}")
  103. net = choose_net(params)
  105. train_dataset, test_dataset = Dataset_LOSO(params['dataset'], params['data_dir'], params['session'],
  106. params['subjects'], params['trial'], i)
  108. loader_train = Data.DataLoader(
  109. dataset=train_dataset,
  110. batch_size=params['batch_size'],
  111. shuffle=True,
  112. num_workers=0
  113. )
  114. loader_test = Data.DataLoader(
  115. dataset=test_dataset,
  116. batch_size=params['batch_size'],
  117. shuffle=True,
  118. num_workers=0
  119. )
  121. print("============================================================================")
  122. print(f"Subject: {i}")
  123. train_losses, val_losses, val_accs, val_cm= train_and_validation(net,
  124. loader_train,
  125. loader_test,
  126. num_epochs=params['epoch'],
  127. lr=params['lr'],
  128. weight_decay=params['weight_decay'],
  129. device=params['device'])
  131. sub_train_loss.append(train_losses)
  132. sub_val_loss.append(val_losses)
  133. sub_val_acc.append(val_accs)
  134. sub_cm.append(val_cm)
  137. # 避免params中的device在保存时出错
  138. params_copy = params.copy()
  139. if 'device' in params_copy:
  140. del params_copy['device']
  142. results = {
  143. 'train_loss': sub_train_loss,
  144. 'val_loss': sub_val_loss,
  145. 'val_acc': sub_val_acc,
  146. 'params': params_copy,
  147. 'cm': sub_cm
  148. }
  149. return results
  152. def train_by_KFold(params):
  153. torch.autograd.set_detect_anomaly(True)
  155. sub_train_loss, sub_val_loss, sub_val_acc = [], [], []
  157. sub_cm = []
  158. subjects = params['subjects']
  159. print(f"Starting KFold training for {subjects} subjects with {params['KFold']} folds.")
  161. for i in range(1, subjects + 1):
  162. fold_train_loss, fold_val_loss, fold_val_acc = [], [], []
  163. fold_cm = []
  165. print(f"Training for Subject {i}")
  167. for fold in range(params['KFold']):
  168. net = choose_net(params)
  169. if params['shuffle'] == 'Sample':
  170. train_dataset, test_dataset = Dataset_KFold_Sample(params['dataset'], params['data_dir'],
  171. params['session'], i, params['trial'],
  172. params['KFold'], fold)
  173. elif params['shuffle'] == 'Trial':
  174. train_dataset, test_dataset = Dataset_KFold_Trial(params['dataset'], params['data_dir'],
  175. params['session'], i, params['trial'],
  176. params['KFold'], fold)
  179. loader_train = Data.DataLoader(
  180. dataset=train_dataset,
  181. batch_size=params['batch_size'],
  182. shuffle=True,
  183. num_workers=0
  184. )
  185. loader_test = Data.DataLoader(
  186. dataset=test_dataset,
  187. batch_size=params['batch_size'],
  188. shuffle=True,
  189. num_workers=0
  190. )
  191. print("============================================================================")
  192. print(f"Subject: {i} Fold: {fold}")
  193. train_losses, val_losses, val_accs, val_cm = train_and_validation(net,
  194. loader_train,
  195. loader_test,
  196. num_epochs=params['epoch'],
  197. lr=params['lr'],
  198. weight_decay=params['weight_decay'],
  199. device=params['device'])
  201. fold_train_loss.append(train_losses)
  202. fold_val_loss.append(val_losses)
  203. fold_val_acc.append(val_accs)
  204. fold_cm.append(val_cm)
  207. sub_train_loss.append(fold_train_loss)
  208. sub_val_loss.append(fold_val_loss)
  209. sub_val_acc.append(fold_val_acc)
  210. sub_cm.append(fold_cm)
  213. # 避免params中的device在保存时出错
  214. params_copy = params.copy()
  215. if 'device' in params_copy:
  216. del params_copy['device']
  218. results = {
  219. 'train_loss': sub_train_loss,
  220. 'val_loss': sub_val_loss,
  221. 'val_acc': sub_val_acc,
  222. 'params': params_copy,
  223. 'cm': sub_cm,
  224. }
  225. return results
  229. # 代码运行开始-设置参数
  230. params = {
  231. # -----------网络参数-------------------------------------------------------
  232. 'emb_dim': 48, # embedding dimension of Embedding, Self-Attention, Mamba
  233. 'emb_kernel': 4, # 2D-conv embedding length of Embedding
  234. 'pool': 4, # pool kernel size
  235. 'd_state': 16, # d_state of Mamba
  236. 'd_conv': 4, # d_conv of Mamba
  237. 'expand': 8, # expand of Mamba
  238. 'num_layers': 2, # layer of TAM
  239. 'num_heads': 8, # num head of Self-Attention
  240. 'g_layers': 4, # graph embedding layers
  241. 'dropout': 0.5, # dropout of Embedding, Self-Attention, Mamba
  242. 'lr': 1e-4, # learning rate
  243. 'weight_decay': 1e-6, # L2-norm
  244. 'time': 384, # window size of EEG
  245. # -----------训练参数-------------------------------------------------------
  246. 'seed': 20, # random seed
  247. 'epoch': 500, # training epoch
  248. 'batch_size': 256, # training batch size
  249. 'session': 1, # dataset session: 1/2 (DEAP:Arousal/Valence, DREAMER:Arousal/Valence, CEED)
  250. 'val': "KFold", # experiment validation：LOSO/KFold
  251. 'shuffle': 'Trial', # validation shuffle way: Sample/Trial
  252. 'net': "DSE-TAM", # Choose net：ACRNN/DSE-TAM/Conformer/Deformer/DGCNN/LGGNet/TSception
  253. 'dataset': 'DEAP', # choose dataset: DEAP/DREAMER/CEED
  254. 'feature': "Time", # input feature: Time/DE
  255. }
  257. # 定义一个训练函数映射，根据 params['val'] 选择相应的训练方式
  258. train_funcs = {
  259. "LOSO": train_by_LOSO,
  260. "KFold": train_by_KFold
  261. }
  264. if __name__ == '__main__':
  265. setup_seed(params['seed'])
  266. params = init_params(params)
  268. # 根据 params['val'] 选择对应的训练函数，并执行
  269. train_func = train_funcs.get(params['val'])
  270. if train_func:
  271. results = train_func(params)
  272. save_results(params, results)

Train.py at commit 2a6e199, no license · at the source

Overview

Authors: Ke Liu1,2, Wenrui Zhu1,3, Rui Xu1,3, Wenlong Wang4, Zhuliang Yu5, Wei Wu4
ORCID iDs: Ke Liu, Wenrui Zhu, Rui Xu, Wenlong Wang, Zhuliang Yu, Wei Wu
  1. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  2. Key Laboratory of Big Data Intelligent Computing, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  3. School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  4. Department of Neurology, Songjiang Hospital and Songjiang Research Institute, Shanghai Key Laboratory of Emotions and Affective Disorders, Shanghai Jiao Tong University School of Medicine, Shanghai 201600, China
  5. College of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
Institutions: Chongqing University of Posts and Telecommunications (China); Shanghai Jiao Tong University (China); South China University of Technology (China)
Journal: Cyborg and bionic systems (Washington, D.C.), volume 7, article 0701
Dates: received 21 May 2026; accepted 25 August 2026; published online 28 October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.34133/cbsystems.0701 · PMID 42953001 · PMCID PMC13714321 · OpenAlex W7207705988
Open access: diamond, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: EEG (modality), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Machine learning, Preprocessing, Smoothing, state filtering, decompositions
Topic: Emotion and Mood Recognition (Artificial Intelligence, Computer Science), according to OpenAlex
Citations: not cited yet (Europe PMC); 48 references in the paper

Abstract

Electroencephalogram (EEG)-based emotion recognition plays a pivotal role in brain–computer interfaces and mental health monitoring. However, conventional approaches often neglect the intrinsic spatial correlations among EEG channels and fail to effectively model both global temporal dependencies and the continuity of neural oscillations. To overcome these challenges, we propose the Dynamic Spatial Embedding and Temporal Attention Mamba (DSE-TAM) framework. Our Dynamic Spatial Embedding (DSE) module leverages graph convolutional networks to dynamically capture inter-channel relationships, preserving spatial coherence in EEG representations. Subsequently, the Temporal Attention Mamba module integrates self-attention with state-space models to jointly optimize long-range temporal dependencies and smooth transition dynamics. Extensive experiments on 2 public benchmarks (DEAP and DREAMER) and an in-house CEED dataset demonstrate that DSE-TAM surpasses state-of-the-art methods, achieving accuracies of 79.75% (arousal) and 78.27% (valence) on DEAP, 80.55% (arousal) and 83.91% (valence) on DREAMER, and 73.75% (3-class) on CEED. These results validate the framework’s efficacy in advancing EEG-based emotion decoding for brain–computer interfaces. The source code is available at https://github.com/Raizeroko/DSE-TAM.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

Raizeroko/DSE-TAM

License: none: the authors keep all their rights
State: the link answers, verified on 30 October 2026
Evidence: files inventoried
Commit: 2a6e199978b0a3a87d16a057c42313a78de091ed, 10 December 2025
Languages: Python (15)
Size: 31 files, 15 scripts
Software Heritage: not archived
Found in: the text, “Implementation details”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: PyTorch (13 files), NumPy (7 files), SciPy (5 files), scikit-learn (2 files), Matplotlib (1 file), Pillow (1 file)
Availability: 1 check, the latest on 30 October 2026: the link answers
  • 30 October 2026: the link answers
16 files
At the source: github.com/Raizeroko/DSE-TAM

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Data

Datasets cited

Data Availability

The DREAMER dataset is accessible via https://zenodo.org/records/546113. The CEED dataset, which was collected by the authors, is available upon request to the corresponding authors. Any additional inquiries regarding the data can be directed to the corresponding authors.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 30 October 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 15 references.

Cite

This paper

Liu, K., Zhu, W., Xu, R., Wang, W., Yu, Z., & Wu, W. (2026). Beyond Static Representations: Learning Electroencephalogram Spatiotemporal Dynamics for Enhanced Emotion Recognition. Cyborg and bionic systems (Washington, D.C.), 7, 0701. https://doi.org/10.34133/cbsystems.0701

BibTeX

@article{liu2026beyond,
author = {Liu, Ke and Zhu, Wenrui and Xu, Rui and Wang, Wenlong and Yu, Zhuliang and Wu, Wei},
title = {{Beyond Static Representations: Learning Electroencephalogram Spatiotemporal Dynamics for Enhanced Emotion Recognition}},
journal = {Cyborg and bionic systems (Washington, D.C.)},
year = {2026},
month = oct,
volume = {7},
pages = {0701},
publisher = {AAAS Science Partner Journal Program},
issn = {2097-1087},
doi = {10.34133/cbsystems.0701},
url = {https://doi.org/10.34133/cbsystems.0701},
pmid = {42953001},
pmcid = {PMC13714321}
}

RIS

TY - JOUR
AU - Liu, Ke
AU - Zhu, Wenrui
AU - Xu, Rui
AU - Wang, Wenlong
AU - Yu, Zhuliang
AU - Wu, Wei
TI - Beyond Static Representations: Learning Electroencephalogram Spatiotemporal Dynamics for Enhanced Emotion Recognition
T2 - Cyborg and bionic systems (Washington, D.C.)
J2 - Cyborg Bionic Syst
PY - 2026
DA - 2026/10/28
VL - 7
SP - 0701
SN - 2097-1087
PB - AAAS Science Partner Journal Program
DO - 10.34133/cbsystems.0701
UR - https://doi.org/10.34133/cbsystems.0701
LA - en
ER -

CSL-JSON

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"title": "Beyond Static Representations: Learning Electroencephalogram Spatiotemporal Dynamics for Enhanced Emotion Recognition",
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"author": [
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"volume": "7",
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"DOI": "10.34133/cbsystems.0701",
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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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