مشاوره قبولی ارشد برق

دانلود کتاب پردازش سیگنال و یادگیری ماشین برای رابط های مغز و ماشین

دانلود رایگان کتاب پردازش سیگنال و یادگیری ماشین برای رابط های مغز و ماشین (سال 2018)

Signal Processing and Machine Learning for Brain-Machine Interfaces by Tanaka and Arvaneh

 

 

 

مشاوره قبولی ارشد برق

تعداد صفحات : 356
نویسندگان : Toshihisa Tanaka , Mahnaz Arvaneh
ویرایش : سال 2018
زبان : انگلیسی

 

Brain-machine interfacing or brain-computer interfacing (BMI/BCI) is an emerging and challenging technology used in engineering and neuroscience. The ultimate goal is to provide a pathway from the brain to the external world via mapping, assisting, augmenting or repairing human cognitive or sensory-motor functions.

In this book an international panel of experts introduce signal processing and machine learning techniques for BMI/BCI and outline their practical and future applications in neuroscience, medicine, and rehabilitation, with a focus on EEG-based BMI/BCI methods and technologies. Topics covered include discriminative learning of connectivity pattern of EEG; feature extraction from EEG recordings; EEG signal processing; transfer learning algorithms in BCI; convolutional neural networks for event-related potential detection; spatial filtering techniques for improving individual template-based SSVEP detection; feature extraction and classification algorithms for image RSVP based BCI; decoding music perception and imagination using deep learning techniques; neurofeedback games using EEG-based Brain-Computer Interface Technology; affective computing system and more.

 

Chapters

1 Brain–computer interfaces and electroencephalogram: basics and practical issues

2 Discriminative learning of connectivity pattern of motor imagery EEG

3 An experimental study to compare CSP and TSM techniques to extract features during motor imagery tasks

4 Robust EEG signal processing with signal structures

5 A review on transfer learning approaches in brain–computer interface

6 Unsupervised learning for brain–computer interfaces based on event-related potentials

7 Covariate shift detection-based nonstationary adaptation in motor-imagery-based brain–computer interface

8 A BCI challenge for the signal-processing community: considering the user in the loop

9 Feedforward artificial neural networks for event-related potential detection

10 Signal models for brain interfaces based on evoked response potential in EEG

11 Spatial filtering techniques for improving individual template-based SSVEP detection

12 A review of feature extraction and classification algorithms for image RSVP-based BCI

13 Decoding music perception and imagination using deep-learning techniques

14 Neurofeedback games using EEG-based brain–computer interface technology

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