Outputs (4)

Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation (2019)
Presentation / Conference Contribution
Aznan, N., Connolly, J., Al Moubayed, N., & Breckon, T. (2019, May). Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation. Presented at 2019 IEEE International Conference on Robotics and Automation (ICRA), Montreal, Canada

This paper addresses the challenge of humanoid robot teleoperation in a natural indoor environment via a Brain-Computer Interface (BCI). We leverage deep Convolutional Neural Network (CNN) based image and signal understanding to facilitate both real-... Read More about Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation.

Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification (2019)
Presentation / Conference Contribution
Aznan, N., Atapour-Abarghouei, A., Bonner, S., Connolly, J., Al Moubayed, N., & Breckon, T. (2019, December). Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification. Presented at International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary

Despite significant recent progress in the area of Brain-Computer Interface (BCI), there are numerous shortcomings associated with collecting Electroencephalography (EEG) signals in real-world environments. These include, but are not limited to, subj... Read More about Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification.

On the Relative Contribution of Deep Convolutional Neural Networks for SSVEP-based Bio-Signal Decoding in BCI Speller Applications (2019)
Journal Article
Podmore, J., Breckon, T., Aznan, N., & Connolly, J. (2019). On the Relative Contribution of Deep Convolutional Neural Networks for SSVEP-based Bio-Signal Decoding in BCI Speller Applications. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(4), 611-618. https://doi.org/10.1109/tnsre.2019.2904791

Brain-computer interfaces (BCI) harnessing Steady State Visual Evoked Potentials (SSVEP) manipulate the frequency and phase of visual stimuli to generate predictable oscillations in neural activity. For BCI spellers, oscillations are matched with alp... Read More about On the Relative Contribution of Deep Convolutional Neural Networks for SSVEP-based Bio-Signal Decoding in BCI Speller Applications.

On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks (2018)
Presentation / Conference Contribution
Aznan, N., Bonner, S., Connolly, J., Al Moubayed, N., & Breckon, T. (2018, October). On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks. Presented at 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018), Miyazaki, Japan

Electroencephalography (EEG) is a common signal acquisition approach employed for Brain-Computer Interface (BCI) research. Nevertheless, the majority of EEG acquisition devices rely on the cumbersome application of conductive gel (so-called wet-EEG)... Read More about On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks.