Brain-Computer Interface: Using Deep Learning Applications 1st Edition

Brain-Computer Interface: Using Deep Learning Applications 1st Edition
– Ebook Instant Download/Delivery ISBN(s): 9781119857204,1119857201, 9781119857754

Product details:

• ISBN 13:9781119857754
• Author:  M. G. Sumithra, Rajesh Kumar Dhanaraj , Mariofanna Milanova , Balamurugan Balusamy , Chandran Venkatesan

The brain-computer interface (BCI) is an emerging technology that is developing to be more functional in practice. The aim is to establish, through experiences with electronic devices, a communication channel bridging the human neural networks within the brain to the external world. For example, creating communication or control applications for locked-in patients who have no control over their bodies will be one such use. Recently, from communication to marketing, recovery, care, mental state monitoring, and entertainment, the possible application areas have been expanding. Machine learning algorithms have advanced BCI technology in the last few decades, and in the sense of classification accuracy, performance standards have been greatly improved. For BCI to be effective in the real world, however, some problems remain to be solved.

Table contents:

1 Introduction to Brain–Computer Interface: Applications and Challenges

2 Introduction: Brain–Computer Interface and Deep Learning

3 Statistical Learning for Brain–Computer Interface

4 The Impact of Brain–Computer Interface on Lifestyle of Elderly People

5 A Review of Innovation to Human Augmentation in Brain-Machine Interface – Potential, Limitation, and Incorporation of AI

6 Resting-State fMRI: Large Data Analysis in Neuroimaging

7 Early Prediction of Epileptic Seizure Using Deep Learning Algorithm

8 Brain–Computer Interface-Based Real-Time Movement of Upper Limb Prostheses Topic: Improving the Quality of the Elderly with Brain-Computer Interface

9 Brain–Computer Interface-Assisted Automated Wheelchair Control Management-Cerebro: A BCI Application

10 Identification of Imagined Bengali Vowels from EEG Signals Using Activity Map and Convolutional Neural Network

11 Optimized Feature Selection Techniques for Classifying Electrocorticography Signals

12 BCI – Challenges, Applications, and Advancements

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