Brain-Computer Interfaces (BCI) are difficult to reproduce and lack benchmarking datasets, which hinders rapid progress and dissemination of ideas and best practices in the domain. The Mother of All BCI Benchmarks, also known as MOABB, constitutes a significant step towards standardization and increased reproducibility in BCI research. MOABB provides benchmarking data sets, algorithms, and code for people to use freely in their own research. It contains 36 open data sets and 30 machine learning pipelines intended to provide easier access to researchers and make starting your BCI research much more efficient. My project is to reproduce the MOABB study to learn about BCI research and reproducible science. I learned basic Python to rerun the code provided by MOABB and assess each algorithm on specific datasets. I also mastered various machine learning (ML) techniques, preprocessing pipelines, and feature extraction methods for EEG data, studying the principles, strengths, and limitations of each approach. BCI approaches I have familiarized myself with involve Raw Signal-Based Pipelines, Riemannian Geometry, and Deep Learning techniques. As an end result of my project, I was able to fully understand and successfully replicate the MOABB study.