2 research outputs found
Detection of the onset of upper-limb movements based on the combined analysis of changes in the sensorimotor rhythms and slow cortical potentials
[Objective] Characterizing the intention to move by means of electroencephalographic activity
can be used in rehabilitation protocols with patients’ cortical activity taking an active role during
the intervention. In such applications, the reliability of the intention estimation is critical both in
terms of specificity ‘number of misclassifications’ and temporal accuracy. Here, a detector of the
onset of voluntary upper-limb reaching movements based on the cortical rhythms and the slow
cortical potentials is proposed. The improvement in detections due to the combination of these
two cortical patterns is also studied.[Approach] Upper-limb movements and cortical activity were
recorded in healthy subjects and stroke patients performing self-paced reaching movements. A
logistic regression combined the output of two classifiers: (i) a naïve Bayes classifier trained to
detect the event-related desynchronization preceding the movement onset and (ii) a matched
filter detecting the bereitschaftspotential. The proposed detector was compared with the detectors
by using each one of these cortical patterns separately. In addition, differences between the
patients and healthy subjects were analysed.[Main results] On average, 74.5 ± 13.8% and 82.2 ±
10.4% of the movements were detected with 1.32 ± 0.87 and 1.50 ± 1.09 false detections
generated per minute in the healthy subjects and the patients, respectively. A significantly better
performance was achieved by the combined detector (as compared to the detectors of the two
cortical patterns separately) in terms of true detections (p = 0.099) and false positives
(p = 0.0083).[Significance] A rationale is provided for combining information from cortical
rhythms and slow cortical potentials to detect the onsets of voluntary upper-limb movements. It
is demonstrated that the two cortical processes supply complementary information that can be
summed up to boost the performance of the detector. Successful results have been also obtained
with stroke patients, which supports the use of the proposed system in brain–computer interface
applications with this group of patients.This work has been funded by grant from the Spanish Ministry of Science and Innovation CONSOLIDER INGENIO, project HYPER (Hybrid NeuroProsthetic and NeuroRobotic Devices for Functional Compensation and Rehabilitation of Motor Disorders, CSD2009-00067), from Proyectos Cero of FGCSIC, Obra Social la Caixa, CSIC, and from the project PIE 201050E087.Peer reviewe