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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 با رتبه های برتر ارشد و دکتری
- برنامهریزی کاملاً اختصاصی و منطبق بر شرایط شما
- پشتیبانی و پیگیری ۲۴ ساعته توسط رتبه های تک رقمی
- معرفی بهترین منابع و آموزش تکنیک های تست زنی
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
مشاوره قبولی در کنکور ارشد و دکتری مهندسی برق
ارسال دیدگاه