Research on brain-to-computer signals presented at international conference
Eli Kinney-Lang, MMEC PhD student, presented his work in South Korea this month winning 3rd prize.
We are delighted to announce that Eli Kinney-Lang, a PhD student at the School of Engineering and a member of the Muir Maxwell Epilepsy Centre, presented his work as a finalist for the International Conference of Engineering in Medicine and Biology this month.
Eli was one of 15 finalists who had been short-listed from a pool of up to 2000 applications that were submitted for the prestigious conference. This year it was held on Jeju Island, South Korea.
Each finalist was up for the chance to win 1st ($1000), 2nd ($750) and 3rd ($500) place prizes. Eli was awarded 3rd place for his presentation – a fantastic result! The conference also provided an opportunity for researchers to share their work with others and develop ideas for further research.
Eli (second from left) and some of the other finalists of the conference
Eli’s work focuses on Brain-Computer Interfaces (BCI) – a technology that translates communication between the brain and a computer. BCIs are currently used around the world for individuals with movement disorders. This includes patients who have little to no function of their limbs after experiencing a stroke.
Whilst much work has been done to improve BCIs in this area, very few applications have examined how BCIs can be used for children with epilepsy. Eli’s work specifically concerns whether electroencephalograms (EEGs), can be used to predict cognitive abilities in this group of patients.
It is hoped that Eli’s findings will someday be implemented into the care of children who face psychological challenges as a result of epilepsy.
One of the biggest challenges is to differentiate between a child’s normal developmental changes and the rehabilitation changes which are happening in the brain. My work aims to create a ‘developmental snapshot’ of these changes using clues from the electric signals, which are emitted from the brain. Identifying these snapshots may then help distinguish the important rehabilitation indicators, providing doctors with a far better analysis of their patient’s progress. Eli Kinney-Lang, PhD Student
