Mahmoud (Mo) Abdellahi

My Story

I studied computer science during my bachelor’s and master’s degrees when I worked on signal processing, multi-variate data analyses, and machine learning, these were coupled with neuroscience and psychology during my PhD. I like learning about machine learning, deep learning, the human brain, and signal processing.

My research at NaPS involves the analyses of EEG signals during wake and sleep and building pipelines for the sake of detecting memory reactivation in human sleep and studying the characteristics of this reactivation. We have done this in both SWS and REM sleep and we were able to relate this reactivation to sleep phenomena such as sleep spindles, slow oscillations, and theta activity to know when to optimally deliver cues to elicit reactivation. In REM sleep, we were able to show that memory reactivation is detectable after sound cues in human sleep. Kindly, have a look at some of my recent work for more details.

I worked on EEG methods to understand how the human brain processes loudness information at University College London (UCL).

I am a computer science lecturer at cairo university, the faculty of computers and artificial intelligence where I teach: brain computer interfacing, supervised learning, machine learning and deep learning, Natural language processing.

I developed a Matlab toolbox for the analysis of EEG signals and multivariate data. This toolbox is called ‘Lively Vectors’ and can be used to perform several steps from segmenting and cleaning data all the way to applying machine and deep learning models and reporting results and statistically correcting for multiple comparisons with methods like cluster-based permutations, I have explained some of its functionalities and provided some tutorials in some videos: link. With this toolbox different analyses are implemented for temporal and spectral analyses and phase coherence. LV is now available along with examples and description on GitHub: link.

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