My neuroscience specialisation is within the area of non-invasive electrophysiological measurements related to thought and emotion in alcohol-related disorders and in normal daily life. My skill-set encompasses expert domain knowledge of biological data-acquisition, biomedical signal-processing, biostatistics, and identification of bioelectrical changes related to the human mind and human behaviour. You can read my doctoral thesis (see here).

I have extensive experience in cognitive and behavioural sciences, working with perceptual and decision-making tasks. I am also have extensive hand-on experience with a range of eye-tracking data-acquisition set-ups. Methodologically, I am interested in source-level connectivity measurements derived from the electroencephalogram. Specifically, this involves questions related to spatial filters and resultant connectivity maps, the use of graph theory to identify connectivity patterns, and the application of multivariate parametric approaches to model EEG signals.

Other interests:

• application of EEG/machine-learning approaches to measure effectiveness of audio-visual content delivery (with partial application to marketing and advertising);
• musical processing and perception in the human nervous system;
• decision-making paradigms and analysis of such data in the context of consumer neuroscience – the relation between decision-making processes and the dynamics of the electrocortical field.

With the advent of deep-learning, I also seek to understand how machine-learning and neural networks can be used to analyse audio-visual information and acquired neural signals. I believe the future of EEG will incorporate deep-learning models deployed at scale with wearable devices that can monitor and track cognitive function.