{"product_id":"brain-computer-interfaces-jamie-flux-9798346020264","title":"Brain-Computer Interfaces: Programming Real-Time Neural Interaction Systems With Python","description":"\u003cp\u003eDiscover the cutting-edge realm of Brain-Computer Interfaces (BCIs) with this comprehensive guide that delves deep into the programming and implementation of real-time neural interaction systems. Whether you're a seasoned researcher or an enthusiastic newcomer, this book offers a treasure trove of advanced techniques designed to transform how we interact with neural data. Packed with Python code for each chapter, this resource is perfect for those ready to turn theory into practice.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eKey Features: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cb\u003eComprehensive Coverage: \u003c\/b\u003e Spanning foundational math to advanced neuroscience, this book equips you with the skills needed to excel in BCI development.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003ePractical Python Implementations: \u003c\/b\u003e Apply what you learn immediately with Python scripts tailored to each technique.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eMultidisciplinary Approach: \u003c\/b\u003e Bridging disciplines from signal processing to machine learning and neuroscience, providing a holistic understanding of BCIs.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cb\u003eBook Description: \u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eThis book is your ultimate resource for building real-time neural interaction systems. It offers a depth of knowledge across numerous techniques used in EEG signal analysis, feature extraction, noise cancellation, and much more. With a focus on practical applications, readers can explore detailed explanations paired with Python implementations that facilitate a hands-on approach to learning. Unlock the potential of cutting-edge tools like wavelet transforms, convolutional neural networks, and reinforcement learning, among others, to create systems that respond to neural data in real time.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eWhat You Will Learn: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eMaster the \u003cb\u003eDiscrete Fourier Transform (DFT)\u003c\/b\u003e for real-time frequency domain analysis in BCIs.\u003c\/li\u003e\n\u003cli\u003eHarness the \u003cb\u003eShort-Time Fourier Transform (STFT)\u003c\/b\u003e to create and interpret spectrograms dynamically.\u003c\/li\u003e\n\u003cli\u003eExplore \u003cb\u003ewavelet transforms\u003c\/b\u003e for detailed multiresolution analysis of neural signals.\u003c\/li\u003e\n\u003cli\u003eImplement \u003cb\u003eEmpirical Mode Decomposition (EMD)\u003c\/b\u003e and the \u003cb\u003eHilbert-Huang Transform\u003c\/b\u003e for adaptive signal analysis.\u003c\/li\u003e\n\u003cli\u003eUtilize \u003cb\u003ePrincipal Component Analysis (PCA)\u003c\/b\u003e for effective dimensionality reduction in large neural datasets.\u003c\/li\u003e\n\u003cli\u003eSeparate and mitigate artifacts in neural recordings with \u003cb\u003eIndependent Component Analysis (ICA)\u003c\/b\u003e.\u003c\/li\u003e\n\u003cli\u003eApply the \u003cb\u003eCommon Spatial Patterns (CSP)\u003c\/b\u003e algorithm to optimize feature extraction in motor-imagery BCIs.\u003c\/li\u003e\n\u003cli\u003eMaximize correlations with \u003cb\u003eCanonical Correlation Analysis (CCA)\u003c\/b\u003e in multi-channel signal processing.\u003c\/li\u003e\n\u003cli\u003eDevelop robust neural classifiers using \u003cb\u003eSupport Vector Machines (SVMs)\u003c\/b\u003e.\u003c\/li\u003e\n\u003cli\u003eImprove class separability through \u003cb\u003eLinear Discriminant Analysis (LDA)\u003c\/b\u003e and Fisher's Criterion.\u003c\/li\u003e\n\u003cli\u003eModel temporal dependencies with \u003cb\u003eHidden Markov Models (HMMs)\u003c\/b\u003e in sequential data.\u003c\/li\u003e\n\u003cli\u003eDesign \u003cb\u003eConvolutional Neural Networks (CNNs)\u003c\/b\u003e to learn spatial features in neural data.\u003c\/li\u003e\n\u003cli\u003eUtilize \u003cb\u003eRecurrent Neural Networks (RNNs)\u003c\/b\u003e and \u003cb\u003eLong Short-Term Memory (LSTM)\u003c\/b\u003e units for capturing temporal dynamics.\u003c\/li\u003e\n\u003cli\u003eImplement \u003cb\u003etransfer learning\u003c\/b\u003e to adapt BCI models for different subjects or tasks.\u003c\/li\u003e\n\u003cli\u003eAnalyze EEG signals using \u003cb\u003eRiemannian Geometry\u003c\/b\u003e for enhanced classification.\u003c\/li\u003e\n\u003cli\u003eModel neural connectivity through \u003cb\u003eGraph Theory\u003c\/b\u003e for better network understanding.\u003c\/li\u003e\n\u003cli\u003eEmploy \u003cb\u003eKalman Filters\u003c\/b\u003e for real-time state estimation in dynamic systems.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Jamie Flux\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798346020264\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 11\/11\/2024\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 382\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.12lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.00h x 6.00w x 0.79d","brand":"Jamie Flux","offers":[{"title":"Paperback","offer_id":46907269841151,"sku":"9798346020264","price":49.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0662\/2982\/9887\/files\/img_45c4dd09-b0a5-4910-9ad9-b6e411c18fdb.jpg?v=1748772829","url":"https:\/\/www.whiterainbookhouse.com\/products\/brain-computer-interfaces-jamie-flux-9798346020264","provider":"WR Book House","version":"1.0","type":"link"}