Name
Title | ||
|---|---|---|
BCINet: An Optimized Convolutional Neural Network for EEG-Based Brain-Computer Interface Applications |
Abstract | ||
|---|---|---|
EEG based brain-computer interface (BCI) allows people to communicate and control external devices using brain signals. The application of BCI ranges from assisting in disabilities to interaction in a virtual reality environment by detecting user intent from EEG signals. The major problem lies in correctly classifying the EEG signals to issue a command with minimal requirement of pre-processing and resources. To overcome these problems, we have proposed, BCINet, a novel optimized convolution neural network model. We have evaluated the BCINet over two EEG based BCI datasets collected in mobile brain/body imaging (MoBI) settings. BCINet significantly outperforms the classification for two datasets with up to 20% increase in accuracy while fewer than 75% trainable parameters. Such a model with improved performance while less requirement of computation resources opens the possibilities for the development of several real-world BCI applications with high performance. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/SSCI47803.2020.9308292 | 2020 IEEE Symposium Series on Computational Intelligence (SSCI) |
Keywords | DocType | ISBN |
Convolutional neural network,deep learning,EEG,brain-computer interface,MOBI,cognitive conflict,BCINet | Conference | 978-1-7281-2548-0 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Avinash Kumar Singh | 1 | 31 | 13.77 |
| Xian Tao | 2 | 12 | 3.72 |