Abstract: A collaborative brain-computer interface integrated with an agent-based framework is presented to optimize work efficiency, with key challenges in work-state management for knowledge workers-specifically, inadequate real-time capability, lack of personalization, and poor integration between neuroscience tools and management workflows-being tackled. The system acquires EEG(Electroencephalogram) signals via an 8-channel OpenBCI(Open Brain-Computer Interface) device. Time-frequency images are constructed using short-time Fourier transform and differential entropy for emotion recognition, and then processed by an enhanced Pyramid SR-CNN ( Super-Resolution-Convolutional Neural Network) model for three-class classification ( negative, neutral, or positive), achieving 94. 01% accuracy on the SEED(SJTU Emotion EEG Dataset) dataset. For fatigue monitoring, a three-class classification ( normal, mild, or severe fatigue) is performed based on a weighted θ/β power ratio, with multi-channel spatial weighting strategies incorporated to improve robustness. A low-latency data pathway is established using the LSL(Lab Streaming Layer) and FastAPI WebSocket. The front end visualizes subject states via ECharts and invokes a COZE agent to generate periodic feedback reports. Six subjects performed cognitive tasks of varying intensity to validate system feasibility. Testing results show that the system effectively detects emotional fluctuations and fatigue states, significantly outperforming conventional behavioral monitoring methods. By generating intervention protocols adapted to individual EEG characteristics, the system enhances work efficiency and offers a scientifically grounded technological approach for managing the working states of knowledge workers.

Key words: information processing technology, analytical system, brain-computer Interface, intelligent agent