Brunel University Research Archive(BURA) preserves and enables easy and open access to all
types of digital content. It showcases Brunel's research outputs.
Research contained within BURA is open access, although some publications may be subject
to publisher imposed embargoes. All awarded PhD theses are also archived on BURA.
Browsing by Subject fully connected deep neural networks (CLDNN)
Showing results 1 to 1 of 1
| Issue Date | Title | Author(s) |
|---|---|---|
| 2020 | Applying CLDNN to Time-Frequency Image of EEG Signals to Predict Depth of Anesthesia | Chen, Y-L; Fan, S-Z; Abbod, M; Shieh, J-S |