Decoding EEG Brain Signals using Recurrent Neural Networks

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(as of Dec 25,2024 00:50:12 UTC – Details)

Publisher ‏ : ‎ Grin Verlag (February 21, 2019)
Language ‏ : ‎ English
Paperback ‏ : ‎ 74 pages
ISBN-10 ‏ : ‎ 3668865035
ISBN-13 ‏ : ‎ 978-3668865037
Item Weight ‏ : ‎ 3.87 ounces
Dimensions ‏ : ‎ 5.83 x 0.18 x 8.27 inches

Decoding EEG Brain Signals using Recurrent Neural Networks

Electroencephalography (EEG) is a non-invasive technique used to record electrical activity in the brain. By analyzing EEG data, researchers and scientists can gain insights into brain function, cognitive processes, and neurological disorders.

One approach to analyzing EEG data is using Recurrent Neural Networks (RNNs). RNNs are a type of artificial neural network that is well-suited for sequential data, making them a natural choice for analyzing time-series data like EEG signals.

By training an RNN on EEG data, researchers can decode brain signals and extract valuable information about brain activity. For example, RNNs can be used to classify different mental states, detect abnormalities in brain function, or even predict future brain activity.

Overall, the combination of EEG data and RNNs holds great promise for advancing our understanding of the brain and developing new ways to diagnose and treat neurological disorders. With further research and development, this technology has the potential to revolutionize the field of neuroscience.
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