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# Exploring the Impact of Emotional States on Teamwork Effectiveness in Interprofessional Education Using EEG Data ## Description This repository contains the official implementation and supplementary materials of the paper: **“Exploring the Impact of Emotional States on Teamwork Effectiveness in Interprofessional Education Using EEG Data”** by *Chongye Wang (Ningbo University of Finance and Economics)* and *Wei Liu (Sichuan Provincial Maternity and Child Health Care Hospital)*, 2024. This work proposes a novel **EEG-driven collaborative learning framework** that integrates emotional state modeling with interprofessional teamwork analysis. The approach introduces two major components: a **Collaborative Interprofessional Learning Network (CILN)** and a **Dynamic Interaction Strategy (DIS)**. Together, these enhance both the interpretability and robustness of emotion-aware team collaboration models in interprofessional education (IPE). --- ## Framework Overview ### Collaborative Interprofessional Learning Network (CILN) The **CILN** module aims to model knowledge sharing and complementarity among members from different disciplines. It includes three major layers: - **Learner Embedding Module:** Encodes each participant’s cognitive and emotional features from EEG signals. - **Knowledge Fusion Network:** Performs cross-disciplinary information alignment using a convolution–Transformer hybrid backbone. - **Collaborative Optimization Layer:** Adjusts interaction weights based on team diversity and learning compatibility. *Figure 1 (Page 8)* in the paper illustrates the architecture combining CNN, Transformer, and a pre-trained BART encoder to achieve hierarchical EEG feature extraction and semantic fusion. --- ### Dynamic Interaction Strategy (DIS) The **DIS** module models evolving interactions within teams over time. It contains: - **Adaptive Interaction Modeling:** A Graph Convolutional Network (GCN) integrated with temporal attention to capture relationship dynamics. - **Feedback-Driven Collaboration Refinement:** Adjusts team interactions according to real-time feedback signals. *Figure 2 (Page 10)* visualizes the temporal structure of DIS, showing how EEG-based emotional variation influences collaboration flow. --- ## Theoretical Foundations This study is grounded in: - **Social Constructivism Theory** - **Affective Events Theory (AET)** - **Multimodal Learning Theory** The framework combines emotional and cognitive features through a joint optimization function that balances: - **Diversity Regularization (Ldiv)** – promoting heterogeneous team perspectives. - **Compatibility Loss (Lcompat)** – ensuring effective task-oriented cooperation. A dynamic update rule adjusts the interaction matrix over time, enabling continuous adaptation to team feedback. --- ## Experimental Setup ### Datasets The model is trained and evaluated on four benchmark EEG-based emotion datasets: - **DEAP** - **AMIGOS** - **DREAMER** - **ASCERTAIN** These datasets collectively represent a wide range of emotional and cognitive patterns relevant to teamwork and collaborative learning. ### Baseline Models For comparison, several mainstream models were tested: - CNN - BiLSTM - GRU - Transformer - BERT - RoBERTa --- ## Experimental Results | Dataset | Accuracy | F1 Score | AUC | Improvement over RoBERTa | |--------------|-----------|-----------|-------|---------------------------| | DEAP | 93.45% | 92.12% | 93.80% | +2.6% | | AMIGOS | 92.33% | 91.45% | 93.05% | +1.7% | | DREAMER | 92.67% | 91.34% | 93.01% | +2.2% | | ASCERTAIN | 90.89% | 89.78% | 92.34% | +3.4% | *Tables 1 and 2 (Page 19)* of the paper report detailed performance metrics. The proposed **CILN + DIS** combination consistently surpasses baseline methods in all datasets. --- ## Ablation Study An ablation analysis (Page 20) demonstrates that removing any core component (embedding, fusion, or interaction) causes a 4–5% accuracy drop. This confirms the synergistic effect of all three modules in improving team emotion recognition and collaboration modeling. --- ## Key Findings - **Innovation:** First framework integrating EEG-based emotional modeling with interprofessional team learning. - **Performance:** Achieves significant improvements in classification accuracy and robustness across multiple EEG benchmarks. - **Interpretability:** Visual attention maps explain how emotional cues influence teamwork effectiveness. - **Future Directions:** Lightweight deployment using wearable EEG devices and real-time feedback integration. --- ## Mathematical Formulation The learning process is formalized as a multi-objective optimization problem: - Utility Function **U(K, C)** captures team learning gain based on knowledge matrix *K* and collaboration matrix *C*. - Interaction matrix *C* evolves over time using adaptive feedback coefficient *ρt*. - Final objective: maximize *U(K, C)* while minimizing regularization terms *(Ldiv + Lcompat)*. --- ## Requirements - Python ≥ 3.9 - PyTorch ≥ 2.1 - NumPy, Pandas, Matplotlib, tqdm, TensorBoard - GPU: NVIDIA A100 or equivalent for full-scale EEG model training --- ## Project Structure The core implementation consists of five main Python scripts: - **`train.py`** – Entry point for model training. Handles training loops, optimization, checkpoint saving, and logging (≈100 lines). - **`model.py`** – Defines the neural network architecture. Includes feature extraction layers, attention mechanisms, and forward computation (≈100 lines). - **`dataset.py`** – Manages dataset loading and preprocessing. Implements custom `Dataset` and `DataLoader` classes for EEG and image inputs (≈100 lines). - **`utils.py`** – Contains helper utilities and evaluation metrics. Provides functions for accuracy, F1 computation, checkpoint management, and visualization (≈100 lines). - **`inference.py`** – Performs inference using trained models. Loads saved weights, processes test data, and outputs prediction results (≈100 lines). --- ## Results Visualization *Figure 4 (Page 15)* demonstrates the evolving team collaboration graph under the DIS mechanism. It shows stronger node connectivity when team emotion alignment increases, indicating that emotional coherence directly enhances collaborative performance. --- ## Citation If you use this work or build upon it, please cite the following: **Wang, C., & Liu, W. (2024).** *Exploring the Impact of Emotional States on Teamwork Effectiveness in Interprofessional Education Using EEG Data.* *PLOS ONE (under revision).* --- ## License This project is released under the **MIT License**. Please see the LICENSE file for details. --- ## Authors - **Chongye Wang** – Methodology, Implementation, and Experiments - **Wei Liu** – Validation, Writing, and Supervision --- ## Acknowledgments This study was supported by *Ningbo University of Finance and Economics* and *Sichuan Provincial Maternity and Child Health Care Hospital*. The authors declare no conflict of interest.

You are given a string s of even length consisting of digits from 0 to 9, and two integers a and b. You can apply either of the following two operations any number of times and in any order on s: Add a to all odd indices of s (0-indexed). Digits post 9 are cycled back to 0. For example, if s = "3456" and a = 5, s becomes "3951". Rotate s to the right by b positions. For example, if s = "3456" and b = 1, s becomes "6345". Return the lexicographically smallest string you can obtain by applying the above operations any number of times on s. A string a is lexicographically smaller than a string b (of the same length) if in the first position where a and b differ, string a has a letter that appears earlier in the alphabet than the corresponding letter in b. For example, "0158" is lexicographically smaller than "0190" because the first position they differ is at the third letter, and '5' comes before '9'.

You are given an integer array nums and an integer k. You are allowed to perform the following operation on each element of the array at most once: Add an integer in the range [-k, k] to the element. Return the maximum possible number of distinct elements in nums after performing the operations.

You are given a string s and an integer k. First, you are allowed to change at most one index in s to another lowercase English letter. After that, do the following partitioning operation until s is empty: Choose the longest prefix of s containing at most k distinct characters. Delete the prefix from s and increase the number of partitions by one. The remaining characters (if any) in s maintain their initial order. Return an integer denoting the maximum number of resulting partitions after the operations by optimally choosing at most one index to change.

MC NeoForge 1.21.1 Java Runtime - Settings are applied in CursedForge Launcher