Title :
A new descriptor of neuroelectrical activity during BCI-assisted motor imagery-based training in stroke patients
Author :
Petti, M. ; Mattia, D. ; Pichiorri, F. ; Toppi, J. ; Salinari, S. ; Babiloni, F. ; Astolfi, L. ; Cincotti, F.
Author_Institution :
Dept. of Comput., Control & Manage. Eng., Univ. of Rome “Sapienza”, Rome, Italy
Abstract :
In BCI applications for stroke rehabilitation, BCI systems are used with the aim of providing patients with an instrument that is capable of monitoring and reinforcing EEG patterns generated by motor imagery (MI). In this study we proposed an offline analysis on data acquired from stroke patients subjected to a BCI-assisted MI training in order to define an index for the evaluation of MI-BCI training session which is independent from the settings adopted for the online control and which is able to describe the properties of neuroelectrical activations across sessions. Results suggest that such index can be adopted to sort the trails within a session according to the adherence to the task.
Keywords :
bioelectric potentials; brain-computer interfaces; electroencephalography; medical disorders; medical signal processing; neurophysiology; patient monitoring; patient rehabilitation; BCI applications; BCI systems; BCI-assisted MI training; BCI-assisted motor imagery-based training; EEG pattern monitoring; EEG pattern reinforcing; MI-BCI training session; neuroelectrical activations; neuroelectrical activity descriptor; offline analysis; online control; stroke patients; stroke rehabilitation; Electroencephalography; Image color analysis; Indexes; Market research; Monitoring; Scalp; Training;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE
Conference_Location :
Chicago, IL
DOI :
10.1109/EMBC.2014.6943828
