Weighted multi-output randomized neural networks with a feature memory pool for electroencephalogram signal classification

Key Points

• EEG signal classification accuracy improves with a weighted multi-output approach, enhancing neural network performance.
• Utilizing a unique feature memory pool allows the model to effectively handle diverse EEG data characteristics.
• Assessment of the neural network model reveals significant advancements over traditional methods, emphasizing its efficacy.
• Potential applications may lead to better diagnostic tools in neuroscience, facilitating real-time brain signal interpretation.