February 2022
Intermediate to advanced
392 pages
13h 2m
English
Content preview from Artificial Intelligence-Based Brain-Computer Interface
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9: EEG-based deep learning neural net for apnea detection
Govinda Rao Locharlaa; Revathi Pogirib; Jaya Prakash Allamc a Department of ECE, GMR Institute of Technology, Rajam, Indiab Department of ECE, SVCET, Etcherla, Indiac Department of ECE, NIT Rourkela, Rourkela, India
Abstract
Brain-computer interface (BCI) has gained popularity for few decades in identifying brain disorders such as stress, apnea, seizure, and dizziness. Early detection of such disorders with proper medication may increase the patients’ quality of life. However, manual analysis of the recorded electroencephalogram (EEG) signals collected from the scalp is a complex task. Therefore, an automated tool for the EEG signals’ analysis and classification is helpful ...
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