Brain-computer interface : using deep learning applications /
edited by M.G. Sumithra, Rajesh Kumar Dhanaraj, Mariofanna Milanova, Balamurugan Balusamy and Chandran Venkatesan.
- 1 online resource.
Front Matter -- Introduction to Brain-Computer Interface / Jyoti R Munavalli, Priya R Sankpal, A Sumathi, Jayashree M Oli -- Introduction / Muskan Jindal, Eshan Bajal, Areeba Kazim -- Statistical Learning for Brain-Computer Interface / Lalit Kumar Gangwar, Ankit, A John, E Rajesh -- The Impact of Brain-Computer Interface on Lifestyle of Elderly People / Zahra Alidousti Shahraki, Mohsen Aghabozorgi Nafchi -- A Review of Innovation to Human Augmentation in Brain-Machine Interface - Potential, Limitation, and Incorporation of AI / T Graceshalini, S Rathnamala, M Prabhanantha Kumar -- Resting-State fMRI / M Menagadevi, S Mangai, S Sudha, D Thiyagarajan -- Early Prediction of Epileptic Seizure Using Deep Learning Algorithm / T Jagadesh, A Reethika, B Jaishankar, MS Kanivarshini -- Brain-Computer Interface-Based Real-Time Movement of Upper Limb Prostheses Topic / S Vairaprakash, S Rajagopal -- Brain-Computer Interface-Assisted Automated Wheelchair Control Management-Cerebro / Sudhendra Kambhamettu, Meenalosini Vimal Cruz, S Anitha, S Sibi Chakkaravarthy, K Nandeesh Kumar -- Identification of Imagined Bengali Vowels from EEG Signals Using Activity Map and Convolutional Neural Network / Rajdeep Ghosh, Nidul Sinha, Souvik Phadikar -- Optimized Feature Selection Techniques for Classifying Electrocorticography Signals / B Paulchamy, R Uma Maheshwari, D Sudarvizhi AP (Sr G), R Anandkumar AP (Sr G), G Ravi -- BCI - Challenges, Applications, and Advancements / R Remya, MG Sumithra -- Index
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).