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Download BCI DeepLearning by ['apurv shukla'] in PDF format completely FREE. No registration required, no payment needed. Get instant access to this valuable resource on PDFdrive.to!
About BCI DeepLearning
This document proposes a method to classify motor imaginary EEG signals using deep learning. It combines a convolutional neural network (CNN) to extract spatial and frequency features from EEG power spectrum graphs, and a long short-term memory (LSTM) network to extract temporal features. The CNN extracts features from the EEG graphs, which preserve both the spatial characteristics from electrode positions and the frequency characteristics associated with motor imagery. These features are then input to the LSTM to incorporate timing features. Experimental results show this approach can classify EEG signals by preserving spatial, frequency, and temporal features simultaneously, improving over traditional classification methods.
Detailed Information
| Author: | ['apurv shukla'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 9 |
| Language: | English |
| Format: | |
| Price: | FREE |
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