Project Abstract

Motivation and Background:-The motivation behind this endeavour stems from recognizing EEG’s unparalleled ability to capture brain dynamics with millisecond temporal resolution.Unlike other neuroimaging techniques like fMRI,EEG excels at tracking rapid changes in brain states crucial for understanding mental processes such as attention, perception and response inhibition.The integration of AI and EEG data offers a promising avenue to decode the complexities of brain function and behaviour.EEG based AI has garnered attention for its potential in neuroscience,Cognitive psychology and clinical practice.Research demonstrates AI efficacy in decoding mental states like attention, memory and emotion from EEG signals.The proposition of this research:-The essential point of this venture is to explore the possibility and viability of AI strategies inremoving spatiotemporal elements from human EEG information during mental assignments.How successfully can AI calculations arrange mental states in light of EEG information?Conclusion:-The discoveries from this examination can possibly propel how we might interpret cerebrum capability and advise the improvement regarding novel Neurotechnologies for clinical and research applications.

Keywords: Artificial Intelligence (AI), Machine learning, Machine Learning From Human EEG Data

 Conference Details

Session: Presentation Stream 18 at Presentation Slot 7

Location: GH014 at Wednesday 8th 09:00 – 12:30

Markers: Benjamin Mora, Jen Pearson

Course: MSc Computer Science, Masters PG

Future Plans: I’m looking for work