Detection of the Onset of Voluntary Movements Based on the Combination of ERD and BP Cortical Patterns

The electroencephalographic activity allows the characterization of movement-related cortical processes. This information may lead to novel rehabilitation technologies with the patients’ cortical activity taking an active role during the intervention. For such applications, the reliability of the estimations based on the electroencephalographic activity is critical both in terms of specificity and temporal accuracy. In this study, a detector of the onset of voluntary upper-limb reaching movements based on cortical rhythms and slow cortical potentials is proposed. To that end, upper-limb movements and cortical activity were recorded while participants performed self-paced movements. A logistic regression combined the output of two classifiers: a) a naive Bayes trained to detect the event-related desynchronization at the movement onset, and b) a matched filter detecting the bereitschaftspotential. On average, 74.5±10.8 % of the movements were detected and 1.32 ± 0.87 false detections were generated per minute. The detections were performed with an average latency of -89.9 ± 349.2 ms with respect to the actual movements. Therefore, the combination of two different sources of information (event-related desynchronization and bereitschaftspotential) is proposed as a way to boost the performance of this kind of systems.