Online control of an assistive active glove by slow cortical signals in patients with amyotrophic lateral sclerosis.

A brain-computer interface (BCI) allows users to control external devices using brain signals that can be recorded non-invasively via electroencephalography (EEG). Movement related cortical potentials (MRCPs) are an attractive option for BCI control since they arise naturally during movement execution and imagination, and therefore, do not require an extensive training. This study tested the feasibility of online detection of reaching and grasping using MRCPs for the application in patients suffering from amyotrophic lateral sclerosis (ALS).A BCI system was developed to trigger closing of a soft assistive glove by detecting a reaching movement. The custom-made software application included data collection, a novel method for collecting the input data for classifier training from the offline recordings based on a sliding window approach, and online control of the glove. Eight healthy subjects and two ALS patients were recruited to test the developed BCI system. They performed assessment blocks without the glove active (NG), in which the movement detection was indicated by a sound feedback, and blocks (G) in which the glove was controlled by the BCI system. The true positive rate (TPR) and the positive predictive value (PPV) were adopted as the outcome measures. Correlation analysis between forehead EEG detecting ocular artifacts and sensorimotor area EEG was conducted to confirm the validity of the results.The overall median TPR and PPV were >0.75 for online BCI control, in both healthy individuals and patients, with no significant difference across the blocks (NG versus G).The results demonstrate that cortical activity during reaching can be detected and used to control an external system with a limited amount of training data (30 trials). The developed BCI system can be used to provide grasping assistance to ALS patients.