Signal Processing and Machine Learning for Brain-Machine Interfaces /

This present book covers numerous examples of advanced machine-learning and signal processing algorithms to robustly decode EEG signals, despite their low spatial resolution, their noisy and nonstationary nature. These algorithms are based on a number of advanced techniques including optimal spatial...

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Bibliographic Details
Corporate Author:	Institution of Engineering and Technology
Other Authors:	Arvaneh, Mahnaz (Editor), Tanaka, Toshihisa (Editor)
Format:	eBook
Language:	English
Published:	Stevenage : IET, 2018.
Series:	Control, Robotics & Sensors.
Subjects:	Brain-computer interfaces. Decoders (Electronics) Electroencephalography. Medical technology. Signal processing. brain-computer interfaces. decoding. electroencephalography. medical signal processing. neural net architecture. spatial filters. unsupervised learning. BMI command discrimination EEG signal decoding brain-machine interfaces deep learning low spatial resolution machine learning neural networks optimal spatial filtering parametric modeling signal processing signal structures supervised adaptation supervised connectivity analysis tangent-space mapping transfer learning unsupervised adaptation
Online Access:	Connect to the full text of this electronic book

Signal processing and machine learning for brain-machine interfaces /

Published 2018

Connect to the full text of this electronic book

eBook