| Paper ID | BIO-1.6 |
| Paper Title |
A DEEP SPATIO-TEMPORAL MODEL FOR EEG-BASED IMAGINED SPEECH RECOGNITION |
| Authors |
Pradeep Kumar, Erik Scheme, University of New Brunswick, Canada |
| Session | BIO-1: Brain-Computer Interfaces |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 13:00 - 13:45 |
| Presentation Time: | Tuesday, 08 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Biomedical Imaging and Signal Processing: [BIO-BCI] Brain/human-computer interfaces |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Automatic speech recognition interfaces are becoming increasingly pervasive in daily life as a means of interacting with and controlling electronic devices. Current speech interfaces, however, are infeasible for a variety of users and use cases, such as patients who suffer from locked-in syndrome or those who need privacy. In these case, an interface that works based on envisioned speech, the idea of imagining what one wants to say, could be of benefit. Consequently, in this work, we propose an imagined speech Brain-Computer-Interface (BCI) using Electroencephalogram (EEG) signals. EEG signals are processed using a deep spatio-temporal learning architecture with 1D Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM), respectively. LSTM units are implemented in a many-to-many fashion to produce a time series of imagined speech outputs. Using this series, the performance of the system is boosted using majority vote post-processing to further improve results. The performance is evaluated on two publicly available datasets; one to test the performance of the tuned model, and another to test its generalization to a new dataset. The proposed architecture outperforms previous results with improvements of up to 23.7%. |
