IEMI aims to develop intelligent algorithms for extracting a continuous measure, from the brain activity, related to Mental Imagery (MI) of a physical movement. The project specifically targets stroke survivors in their rehabilitation process towards physical recovery.
Stroke is affecting around 30 000 people in Sweden every year. Despite intensive rehabilitation, a large group continues to live with persistent disabilities. Physical rehabilitation consists of regular training with a physiotherapist to increase mobility and strength of the affected limb. During training, patients are encouraged to simultaneously imagine the trained movements. Despite a lot of research showing the importance of MI for physical recovery, a means to measure MI in real time does not yet exist.
This is where IEMI comes in. The extracted measure of mental imagery will serve as crucial decision support for: 1) physiotherapists, who will receive real-time information on the mental engagement of patients during rehabilitation, 2) stroke patients, who will receive real-time feedback to strengthen their MI and directly enhance related brain activations. In addition, collected brain activity data will serve as a basis for developing functional diagnostics tools that can serve as a support for assessing the severity of stroke and deciding appropriate strategy for rehabilitation.
The outcomes of IEMI are expected to yield a prototype system of clinical decision support for enhanced stroke rehabilitation. Expert competences in artificial intelligence and in specialized stroke rehabilitation are merged in IEMI to present a highly innovative technology with the potential to substantially increase the quality of stroke rehabilitation.
|First Name||Last Name||Title|
|Miguel||Leon Ortiz||Doctoral student|
Impact of NSGA-II objectives on EEG feature selection related to motor imagery (Jan 2020) Miguel Leon Ortiz, Christoffer Parkkila, Jonatan Tidare, Ning Xiong, Elaine Åstrand Genetic and Evolutionary Computation Conference (GECCO 2020)
Feature Selection of EEG Oscillatory Activity Related to Motor Imagery Using a Hierarchical Genetic Algorithm (Jun 2019) Miguel Leon Ortiz, Joaquin Ballesteros, Jonatan Tidare, Ning Xiong, Elaine Åstrand IEEE Congress on Evolutionary Computation (IEEE CEC'19)
Discriminating EEG spectral power related to mental imagery of closing and opening of hand (Mar 2019) Jonatan Tidare, Miguel Leon Ortiz, Ning Xiong, Elaine Åstrand THe 9th International IEEE EMBS Conference of Neural Engineering (IEEE NER'19)
Evaluation of Closed-Loop Feedback System Delay: A time-critical perspective for neurofeedback training (Jan 2018) Jonatan Tidare, Elaine Åstrand, Martin Ekström International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018)
Individual working memory capacity traced from multivariate pattern classification of EEG spectral power Elaine Åstrand 40th International conference of the IEEE Engineering in Medicine and Biology Society (IEEE EMBC'18)
|Danderyds Sjukhus AB||Municipalities and others|