Textbook in PDF format Signal processing and machine learning are the primary components of modern neuroscience research, focusing on feature extraction. They enable us to uncover meaningful patterns hidden in the complex, high-dimensional signals generated by brain recordings. In recent years, Python has emerged as one of the most powerful and accessible tools for analyzing and processing neuroscience data. Its flexibility, large ecosystem of scientific libraries, and active community provide researchers with the means to build robust analysis pipelines and to explore brain data in creative and innovative ways. This book is designed as a hands-on guide to applying signal processing and machine learning techniques in neuroscience using Python. It is written for researchers and students with a basic understanding of Python who want to learn how to apply practical methods to real EEG and brain signal data. Topics range from fundamental concepts such as filtering and spectral analysis, to advanced approaches including time-frequency analysis and machine learning for feature extraction. With the increasing availability of affordable EEG devices, neuroscience research has moved beyond the laboratory. Today, brain–computer interface (BCI) applications, neurofeedback systems, and consumer EEG headsets are more accessible than ever. The brain remains one of the greatest scientific mysteries, but this book aims to show how Python can help us begin to unravel its signals and apply them in both research and real-world contexts. Preface Fundamentals of EEG Getting Started with Signal Processing in Python EEG and Visualization Bandpass Filter Smoothing Filters Frequency Analysis Navigating Noise: Strategies for EEG Artefact Removal Real-Time Signal Processing in EEG Application Without Machine Learning Machine Learning: EEG Perspective Dataset for Machine Learning Dataset Preprocessing ML and EEG Case Studies and Applications