Browsing by Author "Chaudhary, Ujwal"
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Item Brain-computer interfaces for communication and rehabilitation(2016-09-01) Chaudhary, Ujwal; Birbaumer, Niels; Ramos-Murguialday, Ander; Medical TechnologiesBrain-computer interfaces (BCIs) use brain activity to control external devices, thereby enabling severely disabled patients to interact with the environment. A variety of invasive and noninvasive techniques for controlling BCIs have been explored, most notably EEG, and more recently, near-infrared spectroscopy. Assistive BCIs are designed to enable paralyzed patients to communicate or control external robotic devices, such as prosthetics; rehabilitative BCIs are designed to facilitate recovery of neural function. In this Review, we provide an overview of the development of BCIs and the current technology available before discussing experimental and clinical studies of BCIs. We first consider the use of BCIs for communication in patients who are paralyzed, particularly those with locked-in syndrome or complete locked-in syndrome as a result of amyotrophic lateral sclerosis. We then discuss the use of BCIs for motor rehabilitation after severe stroke and spinal cord injury. We also describe the possible neurophysiological and learning mechanisms that underlie the clinical efficacy of BCIs.Item Corrigendum: Brain-computer interfaces for communication and rehabilitation(2017-02-17) Chaudhary, Ujwal; Birbaumer, Niels; Ramos-Murguialday, Ander; Medical TechnologiesItem Electrooculogram based sleep stage classification using deep belief network(Institute of Electrical and Electronics Engineers Inc., 2015-09-28) Xia, Bin; Li, Qianyun; Jia, Jie; Wang, Jingyi; Chaudhary, Ujwal; Ramos-Murguialday, Ander; Birbaumer, Niels; Medical TechnologiesIn this work, we used single electrooculogram (EOG) signal to perform automatic sleep scoring. Deep belief network (DBN) and combination of DBN and Hidden Markov Models (HMM) are employed to discriminate sleep stages. Under the leave-one-out protocol, the average accuracy of DBN and DBN-HMM are 77.7% and 83.3% for all sleep stages, respectively. On the other hand, we found the EOG signal not only contribute to identify stages of Awake and rapid eye movement, also contribute to discriminate stage 2 and slow wave sleep stage.