This competition aims to develop a machine learning model to predict inertial signal values from EEG data collected from a person with spinal cord injury using a Lokomat exoskeleton. The predictions will be evaluated based on their **root mean squared error (RMSE)** between the predicted and actual values.

## Submission Format
Participants are required to submit their predictions in a CSV file with the following format:

The file should contain a header row with two columns: "id" and "prediction".
The "id" column should contain unique identifiers for each test sample.
The "prediction" column should contain the predicted inertial signal values for each test sample.

## Evaluation
Submissions will be evaluated using the mean squared error (MSE) between the predicted and actual inertial signal values. The formula for the MSE is as follows:

$$RMSE = \sqrt{1/N * sum((y_{pred} - y_{true})^2)}$$

where N is the total number of test samples, $$y_{pred}$$ is the predicted value, and $$y_{true}$$ is the actual value.

The score for each submission will be calculated based on the RMSE, and the submission with the lowest MSE will be ranked at the top of the leaderboard. The final ranking will be based on the scores of the top five submissions.

Note: In the event of a tie, the submission with the lower run time will be ranked higher.