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Fundamentals Approached Towards Artificial Neural Networks in Brain Computer Interface

Fundamentals Approached Towards Artificial Neural Networks in Brain Computer Interface

Nitish Pathak (Bhagwan Parshuram Institute of Technology, New Delhi, India), Neelam Sharma (Department of CSE-AIML, Maharaja Agrasen Institute of Technology, New Delhi, India), Deepali Rani Sahoo (Manipal Law School, Manipal University Jaipur, India), Farhad Alam (JB Institute of Technology, India), Harishchander Anandaram (Amrita School of Artificial Intelligence, Coimbatore, Amrita Vishwa Vidyapeetham, India), and Kapil Joshi (Uttaranchal University, India)
Copyright: © 2025 |Pages: 20
DOI: 10.4018/979-8-3693-9445-8.ch008
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Abstract

A neural network is essentially an attempt to replicate how the brain works. Neural system theory is based on the idea that several of the most significant features of biological neurons can be extracted and applied to computations in computer order to produce a more simplified model of the brain. An ANN is configured for a specific use, such as pattern recognition or data classification, through a learning process. Learning involves altering the synaptic interactions between neurons in biological systems. Along with several ANN trainings, the fundamental MATLAB processes are presented. The training's goal is to reduce mean square error. T
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