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• W4319875849 endingPage "62" @default.
• W4319875849 startingPage "25" @default.
• W4319875849 abstract "Brain signals or radiations are a relatively new concept that works with information collected via human brain and cognitive activity. These signals like human brain are influenced by all the physical, geographical, emotional, and cognitive activities around it, are individualistic in nature, and follow numerous patterns. Complying with the same Brain–computer interface (BCI), is the area of science where these signals are contemplated to advance the human-computer interaction applications. This area of research ties the cord between human cognitive to computer's processing and speed, aiming to create a unique field in biomedical sciences to introduce infinite applications like, neural rehabilitation, biometric authentication, educational programs, and entertainment applications. Although nascent, Brain–computer interface (BCI) has four major grades of processing steps – signal acquisition, signal pre-processing, feature extraction, and classification. This study firstly, thoroughly elaborates upon its various processing steps while exploring the origin, need, and current stage of development of Brain– computer interface (BCI) with respect to brain signals. Post complete understanding of basic concepts and terminologies of brain signals, Brain–computer interface (BCI) and their interconnection along in the field of bio-medicine; this study elucidates upon primordial methodologies of Brain–computer interface (BCI) along with respective merits and demerits with intensive classification. A differential based analysis is provided to insight into new age deep learning-based method in the field of Brain–computer interface (BCI) while comparing them with primordial techniques. A complete data intensive review is performed for new era deep learning techniques while properly classifying the same into multiple gradations based on deep learning framework and their various versions implemented namely, neural networks (CNN) and recurrent neural networks (RNN), long short-term memory (LSTM) architecture, U-net among other. This chapter also aims to provide real world application, challenges, scope of future growths, avenues of expansion and complete industry specific guide for implementing insights gathered from brain signals in the nascent area of Brain– computer interface (BCI). Lastly, to fathom the performance of Brain–computer interface (BCI) this study also provides its application in multiple case studies with desperate health hazards like brain tumor, Dementias, Epilepsy and Other Seizure Disorders, Stroke and Transient Ischemic Attack (TIA), Alzheimer's Disease, Parkinson's and Other Movement Disorders. This chapter aims to not only provide background, current status, future challenges and case studies but also an application specific perspective in Brain–computer interface (BCI) in the field of bio-medical." @default.
• W4319875849 created "2023-02-11" @default.
• W4319875849 creator A5042528933 @default.
• W4319875849 creator A5066304989 @default.
• W4319875849 creator A5086915814 @default.
• W4319875849 date "2023-02-10" @default.
• W4319875849 modified "2023-10-14" @default.
• W4319875849 title "Introduction" @default.
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