EEG Signal Classification Using Backpropagation Neural Network
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Resource Overview
Original code implementation featuring EEG signal workspace data provided by my supervisor, demonstrating classification using BP neural network - ideal for beginners learning neural networks with practical examples.
Detailed Documentation
This project contains original code implementation with EEG signal workspace data provided by my supervisor. The classification is achieved using a Backpropagation (BP) neural network, which implements gradient descent optimization for weight adjustments. The code demonstrates key neural network components including forward propagation for signal processing and backward error propagation for network training. For those beginning to study neural networks, this project serves as an excellent practical example to understand both the theoretical workings and real-world applications of neural networks, particularly in biomedical signal processing. The implementation includes data preprocessing, network architecture configuration, and performance evaluation metrics.
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