Anticipatory Detection of Self-Paced Rehabilitative Movements in the Same Upper Limb From EEG Signals

Abstract

Currently, one of the challenges in EEG-based brain-computer interfaces (BCI) for neurorehabilitation is the recognition of the intention to perform different movements from the same limb. This would allow finer control of neurorehabilitation and motor recovery devices by end-users. To address this issue, we assess the feasibility of recognizing two rehabilitative right upper-limb movements from pre-movement EEG signals. These rehabilitative movements were performed self-selected and self-initiated by the users using a motor rehabilitation robotic device. This work proposes anticipatory detection scenarios that discriminate EEG signals corresponding to non-movement state and movement intentions of two same-limb movements. The studied movements were discriminated above the empirical chance levels for all proposed detection scenarios. Percentages of correctly anticipated trials ranged from 64.3% to 77.0%, and the detection times ranged from 620 to 300 ms prior to movement initiation. The results of these studies indicate that it is possible to detect the intention to perform two different movements of the same upper limb and non-movement state. Based on these results, the decoding of the movement intention could potentially be used to develop more natural and intuitive robot-assisted neurorehabilitation therapies.

Keywords

• Brain-computer interfaces
• EEG signals
• movement intention
• neurorehabilitation
• robot-assisted therapies
• self-paced movement