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There is a programming language with only four operations and one variable X: ++X and X++ increments the value of the variable X by 1. --X and X-- decrements the value of the variable X by 1. Initially, the value of X is 0. Given an array of strings operations containing a list of operations, return the final value of X after performing all the operations.

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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.