EEG signal processing with Python machine learning techniques for brain-computer interface development Ildar Rakhmatulin, Ganesh R. Naik

Format:

Book

Author/Creator:

Rakhmatulin, Ildar, author.

Naik, Ganesh R., author.

Series:

Brain informatics and health

Brain informatics and health 2367-1750

Language:

English

Subjects (All):

Electroencephalography.

Signal processing.

Machine learning--Industrial applications.

Python (Computer program language).

Physical Description:

1 online resource

Place of Publication:

Singapore Springer [2026]

Summary:

"Unlock the power of brain-computer interfaces (BCIs) with this practical guide to signal processing and machine learning. Learn to decode neural data using Python, from fundamental techniques to cutting-edge algorithms. Master essential libraries, implement real-time processing, and design your own BCI systems. Perfect for students, researchers, and innovators ready to build the future of neurotechnology. From basic signal processing to advanced machine learning techniques, you will learn how to extract meaningful insights from complex neuroscience data. Step-by-step tutorials guide you through real-world applications, empowering you to: Master essential Python libraries for neuroscience data analysis; Implement signal filtering, feature extraction, and neural decoding algorithms; Design and evaluate BCI systems using state-of-the-art machine learning approaches. Whether you are a student, researcher, or entrepreneur, this book provides the tools and knowledge to turn brain signals into actionable insights. With its focus on practical implementation and real-time processing, it's an invaluable resource for anyone looking to harness the potential of BCIs. Don't just read about neurotechnology – learn to build it. Take your first step towards creating the next generation of brain-computer interfaces today"-- Springer Nature Link

Contents:

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

Notes:

Includes bibliographical references

Online resource; title from PDF title page (Springer Nature Link, viewed April 17, 2026)

Other Format:

Print version Rakhmatulin, Ildar EEG signal processing with Python

ISBN:

9789819541775

9819541778

OCLC:

1584527402

Access Restriction:

Restricted for use by site license