Detecting intention to walk in stroke patients from pre-movement EEG correlates

I'm sure our fucking failures of stroke associations will not match up BCIs already out there to see what stroke protocol should be created. You had better be rich so you can hire your own team of researchers to figure out how to use this in stroke rehab. NOONE is going to do this for you. You're screwed, your children are screwed, your grandchildren are screwed, unless YOU pay it foward by repurposing stroke associations for survivors.
http://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-015-0087-4

• Andreea Ioana SburleaEmail author,
• Luis Montesano,
• Roberto Cano de la Cuerda,
• Isabel Maria Alguacil Diego,
• Juan Carlos Miangolarra-Page and
• Javier Minguez

Journal of NeuroEngineering and Rehabilitation201512:113

DOI: 10.1186/s12984-015-0087-4

©  Sburlea et al. 2015

Received: 29 April 2015

Accepted: 23 October 2015

Published: 12 December 2015

Abstract

Background

Most studies in the field of brain-computer interfacing (BCI) for lower limbs rehabilitation are carried out with healthy subjects, even though insights gained from healthy populations may not generalize to patients in need of a BCI.

Methods

We investigate the ability of a BCI to detect the intention to walk in stroke patients from pre-movement EEG correlates. Moreover, we also investigated how the motivation of the patients to execute a task related to the rehabilitation therapy affects the BCI accuracy. Nine chronic stroke patients performed a self-initiated walking task during three sessions, with an intersession interval of one week.

Results

Using a decoder that combines temporal and spectral sparse classifiers we detected pre-movement state with an accuracy of 64 % in a range between 18 % and 85.2 %, with the chance level at 4 %. Furthermore, we found a significantly strong positive correlation (r = 0.561, p = 0.048) between the motivation of the patients to perform the rehabilitation related task and the accuracy of the BCI detector of their intention to walk.

Conclusions

We show that a detector based on temporal and spectral features can be used to classify pre-movement state in stroke patients. Additionally, we found that patients’ motivation to perform the task showed a strong correlation to the attained detection rate of their walking intention.