Define evaluation metrics (e.g., accuracy, sensitivity, specificity, AUC) used to assess the performance of machine learning models in brain signal decoding tasks.

Evaluation metrics are essential tools for assessing the performance of machine learning models in brain signal decoding tasks. These metrics help quantify how well the model is performing in terms of correctly classifying brain signals into different categories, such as cognitive states, neurological conditions, or other relevant labels. In brain signal decoding tasks, where the goal is to interpret brain activity patterns and make predictions based on them, the choice of evaluation metrics is critical to understanding the model's strengths and weaknesses. Here are some commonly used evaluation metrics: 1. Accuracy: Accuracy is one of the most straightforward evaluation metrics and represents the proportion of correctly classified samples over the total number of samples in the dataset. While accuracy provides an overall picture of the model's performance, it may not be the most suitable metric for imbalanced datasets, where one class dominates the others. In such cases, high accuracy can be achieved by simply predicting the majority class, leading to misleading results. 2. Sensitivity (Recall or True Positive Rate): Sensitivity measures the ability of the model to correctly identify positive instances (e.g., dete....