Mohamed Zaky

BSc., MSc

PhD Student

New Zealand Brain Research Institute, Christchurch

PhD Student

Department of Electrical and Computer Engineering, University of Canterbury, Christchurch

An attention lapse is a lapse of responsiveness in which performance is completely disrupted butconsciousness is retained. Attention lapses are behaviourally different from microsleeps, as in an attentionlapse eyes remain open whereas in a microsleep eyes are partially or completely closed. Both attention lapses and microsleeps can result in catastrophic consequences, especially in transportation sector.

Research over the last two decades has investigated the attention lapse phenomenon using behavioural andphysiological means. However, attention lapses need more investigations to separate the different types ofattention lapses physiologically and behaviourally. Hence, the objective of this project is to understand the underlying physiological and behavioural substrates of endogenous (internal) attention lapses which could potentially result in differentiating types of attention lapses. Our research has four hypotheses: first, in a continuous visuomotor task, endogenous attention lapses will show a neural activity pattern characterised by higher activity in default mode network (DMN). Second, in a continues visuomotor task, there will be lower neural activity in the dorsal attention network (DAN) during endogenous attention lapses. Third, the working memory network (WMN) will have substantially less neural activity during mind-blanks than during mindwandering. Fourth, mind-wandering and mind-blanking can be differentiated behaviourally using combined oculometric features in a continuous visuomotor task.

In our lapses taxonomy (Jones & Innes, 2017), both mind-wandering (endogenously diverted attention lapses) and mind-blanking (lost-attention lapses) fall under the attention-lapses category, represented by completetransient cessation of performance but with no eye closure. To explore the hypotheses, we will use previously recorded and identified data from previous lapse studies and exploit fMRI to identify changes in brain activity related to endogenous attention lapses. Using unsupervised learning we will be able to separate different types of attention lapses. Finally, using image/video processing techniques we will analyse the oculometric features of each type. This project will improve our understanding of endogenous attention lapses and get us a step closer to accurate detection/prediction systems that can decrease and eventually prevent fatal accidents.

Abstracts and Short papers

2016

Zaky, M.H., Khedr, M.E., Nasser, A. A. (2016). Effect of extensive training load on the classification accuracy for a three class motor imagery based brain-computer interface. The 3rd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA 2016), Beirut-Lebanon, 211-215. 10.1109/ACTEA.2016.7560141
Zaky, M.H., Nasser, A.A., Khedr, M.E. (2016). Combining feature extraction methods to classify three Motor Imagery tasks. International Conference on Communication, Management and Information Technology (ICCMIT 2016), Cosenza-Italy, 195-202.
Zaky, M.H., Nasser, A.A., Khedr, M.E. (2016). A robust brain computer interface system for classifying multi motor imagery tasks over daily sessions. The 39th International Conference on Telecommunications and Signal Processing, Vienna-Austria, 374-378. 10.1109/TSP.2016.7760900