Brain-computer interface: using deep learning applications
BRAIN-COMPUTER INTERFACE It covers all the research prospects and recent advancements in the brain-computer interface using deep learning. 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 w...
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| Weitere beteiligte Personen: | , , , , |
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| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Hoboken
Wiley-Scrivener
2023
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| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781119857204/?ar |
| Zusammenfassung: | BRAIN-COMPUTER INTERFACE It covers all the research prospects and recent advancements in the brain-computer interface using deep learning. 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. Research focusing on deep learning is anticipated to bring solutions in this regard. Deep learning has been applied in various fields such as computer vision and natural language processing, along with BCI growth, outperforming conventional approaches to machine learning. As a result, a significant number of researchers have shown interest in deep learning in engineering, technology, and other industries; convolutional neural network (CNN), recurrent neural network (RNN), and generative adversarial network (GAN). Audience Researchers and industrialists working in brain-computer interface, deep learning, machine learning, medical image processing, data scientists and analysts, machine learning engineers, electrical engineering, and information technologists. |
| Umfang: | 1 Online-Ressource |
| ISBN: | 9781119857648 1119857643 9781119857204 |
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| spelling | Brain-computer interface using deep learning applications edited by M.G. Sumithra, Rajesh Kumar Dhanaraj, Mariofanna Milanova, Balamurugan Balusamy, Chandran Venkatesan Hoboken Wiley-Scrivener 2023 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BRAIN-COMPUTER INTERFACE It covers all the research prospects and recent advancements in the brain-computer interface using deep learning. 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. Research focusing on deep learning is anticipated to bring solutions in this regard. Deep learning has been applied in various fields such as computer vision and natural language processing, along with BCI growth, outperforming conventional approaches to machine learning. As a result, a significant number of researchers have shown interest in deep learning in engineering, technology, and other industries; convolutional neural network (CNN), recurrent neural network (RNN), and generative adversarial network (GAN). Audience Researchers and industrialists working in brain-computer interface, deep learning, machine learning, medical image processing, data scientists and analysts, machine learning engineers, electrical engineering, and information technologists. Brain-computer interfaces Deep learning (Machine learning) Interfaces cerveau-ordinateur Apprentissage profond Sumithra, M. G. HerausgeberIn edt Dhanaraj, Rajesh Kumar HerausgeberIn edt Milanova, Mariofanna G. HerausgeberIn edt Balusamy, Balamurugan HerausgeberIn edt Venkatesan, Chandran HerausgeberIn edt 1119857201 Erscheint auch als Druck-Ausgabe 1119857201 |
| spellingShingle | Brain-computer interface using deep learning applications Brain-computer interfaces Deep learning (Machine learning) Interfaces cerveau-ordinateur Apprentissage profond |
| title | Brain-computer interface using deep learning applications |
| title_auth | Brain-computer interface using deep learning applications |
| title_exact_search | Brain-computer interface using deep learning applications |
| title_full | Brain-computer interface using deep learning applications edited by M.G. Sumithra, Rajesh Kumar Dhanaraj, Mariofanna Milanova, Balamurugan Balusamy, Chandran Venkatesan |
| title_fullStr | Brain-computer interface using deep learning applications edited by M.G. Sumithra, Rajesh Kumar Dhanaraj, Mariofanna Milanova, Balamurugan Balusamy, Chandran Venkatesan |
| title_full_unstemmed | Brain-computer interface using deep learning applications edited by M.G. Sumithra, Rajesh Kumar Dhanaraj, Mariofanna Milanova, Balamurugan Balusamy, Chandran Venkatesan |
| title_short | Brain-computer interface |
| title_sort | brain computer interface using deep learning applications |
| title_sub | using deep learning applications |
| topic | Brain-computer interfaces Deep learning (Machine learning) Interfaces cerveau-ordinateur Apprentissage profond |
| topic_facet | Brain-computer interfaces Deep learning (Machine learning) Interfaces cerveau-ordinateur Apprentissage profond |
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