110 citations · 185 across the 4 of their papers we have counts for
4 papers
Leveraging Synthetic Subject Invariant EEG Signals for Zero Calibration BCI
Nik Khadijah Nik Aznan, Amir Atapour-Abarghouei, Stephen Bonner +2
Recently, substantial progress has been made in the area of Brain-Computer Interface (BCI) using modern machine learning techniques to decode and interpret brain signals. While Ele…
Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification
Nik Khadijah Nik Aznan, Amir Atapour-Abarghouei, Stephen Bonner +3
Despite significant recent progress in the area of Brain-Computer Interface (BCI), there are numerous shortcomings associated with collecting Electroencephalography (EEG) signals i…
Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation
Nik Khadijah Nik Aznan, Jason D. Connolly, Noura Al Moubayed +1
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 Netwo…
On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks
Nik Khadijah Nik Aznan, Stephen Bonner, Jason D. Connolly +2
In this paper, we propose a novel Convolutional Neural Network (CNN) approach for the classification of raw dry-EEG signals without any data pre-processing. To illustrate the effec…