Decoding EEG Signals during the Observation of Robotic Arm Movements

Recent studies in the domain of invasive brain-computer interfaces (BCIs) have revealed that neural activity recorded during the observation of robotic movements in a reach-and-grasp task carries information that can be utilized to improve the active online decoding of motor intention. In the non-invasive domain, the spectral characteristics of human brain activity during the observation of robotic movements has been widely investigated. However, focusing only on the frequency components of electroencephalography (EEG) for motor control decoding is a poorly suitable strategy due to its scarce temporal resolution. Following a different approach, we explored temporal features of EEG filtered in the delta band (Low-Frequency EEG, or LF-EEG) for the continuous decoding of control-oriented kinematic trajectories. We designed an experimental paradigm aimed at investigating how the observation of center-out target-oriented reaching movements executed by a robotic arm in the 2D plane is encoded in low-frequency EEG signals. By employing machine learning algorithms and novel approaches, we were able to continuously decode the LF-EEG into movement trajectories, achieving performance significantly above chance-level. This confirms that low-frequency neural activity measured non-invasively during a movement observation task contains adequate amounts of movement-related information for BCI applications.