- Promoted by: Anonymous
- Platform: Udemy
- Category: Data Science
- Language: English
- Instructor: Data Science Academy , School of AI
- Duration: 5 hour(s) 30 minute(s)
- Student(s): 108
- Rate 0 Of 5 From 0 Votes
- Expires on: 2025/12/15
-
Price:
54.990
Hands-on deep learning for brain–computer interfaces using EEGNet and real motor imagery EEG data
Unlock your potential with a Free coupon code
for the "Coding the Brain: AI & Machine Learning for BCIs" course by Data Science Academy , School of AI on Udemy.
This course, boasting a 0.0-star rating from 0 reviews
and with 108 enrolled students, provides comprehensive training in Data Science.
Spanning approximately
5 hour(s)
30 minute(s)
, this course is delivered in English
and we updated the information on December 12, 2025.
To get your free access, find the coupon code at the end of this article. Happy learning!
“This course contains the use of artificial intelligence”
Unlock the power of brain–computer interfaces (BCIs) by learning how to decode human intention directly from EEG signals using EEGNet, one of the most widely adopted deep-learning models in neurotechnology. This hands-on course teaches you how to build a complete Motor Imagery Classification pipeline—from loading real EEG datasets to training, evaluating, and deploying a fully functional model.
You will work extensively with the BNCI-Horizon 004 (BCI Competition IV 2a) dataset, a gold-standard benchmark used in academic research and industry. You’ll learn how to perform signal preprocessing, including bandpass filtering, epoch creation, and standardization, followed by constructing a full training workflow using TensorFlow/Keras. The course also covers model optimization, performance evaluation, and interpreting neural patterns that distinguish left-hand, right-hand, feet, and both-hands imagery tasks.
Beyond training EEGNet, you will gain practical experience in real-time BCI concepts, enabling you to extend your model toward interactive control systems. The step-by-step practical labs ensure you not only understand the theory but also build a working BCI system from scratch.
By the end of this course, you will be able to confidently preprocess EEG data, train and validate deep-learning models for motor imagery, and understand how BCIs transform neural activity into real-world applications such as prosthetics, gaming, assistive robotics, and neurofeedback systems.
This course is ideal for anyone interested in AI, neuroscience, machine learning, or human–computer interaction, and requires no prior experience with BCI systems.