Abstract
BackgroundOne of the current challenges in brain-machine interfacing is to characterize and decode upper limb kinematics from brain signals, e.g. to control a prosthetic device. Recent research work states that it is possible to do so based on low frequency EEG components. However, the validity of these results is still a matter of discussion. In this paper, we assess the feasibility of decoding upper limb kinematics from EEG signals in center-out reaching tasks during passive and active movements.MethodsThe decoding of arm movement was performed using a multidimensional linear regression. Passive movements were analyzed using the same methodology to study the influence of proprioceptive sensory feedback in the decoding. Finally, we evaluated the possible advantages of classifying reaching targets, instead of continuous trajectories.ResultsThe results showed that arm movement decoding was significantly above chance levels. The results also indicated that EEG slow cortical potentials carry significant information to decode active center-out movements. The classification of reached targets allowed obtaining the same conclusions with a very high accuracy. Additionally, the low decoding performance obtained from passive movements suggests that discriminant modulations of low-frequency neural activity are mainly related to the execution of movement while proprioceptive feedback is not sufficient to decode upper limb kinematics.ConclusionsThis paper contributes to the assessment of feasibility of using linear regression methods to decode upper limb kinematics from EEG signals. From our findings, it can be concluded that low frequency bands concentrate most of the information extracted from upper limb kinematics decoding and that decoding performance of active movements is above chance levels and mainly related to the activation of cortical motor areas. We also show that the classification of reached targets from decoding approaches may be a more suitable real-time methodology than a direct decoding of hand position.
Highlights
One of the current challenges in brain-machine interfacing is to characterize and decode upper limb kinematics from brain signals, e.g. to control a prosthetic device
This recovery is crucial in order to perform activities of the daily life, so the use of Brain-machine interface (BMI) during the rehabilitation may be a key factor of improvement [3]
Recent works suggest that it is possible to decode hand or arm kinematics from slow cortical potentials (SCPs), i.e., EEG signals oscillations below 2 Hz [17,18,19,20]
Summary
One of the current challenges in brain-machine interfacing is to characterize and decode upper limb kinematics from brain signals, e.g. to control a prosthetic device. Brain-Machine Interfaces (BMIs) are devices aimed at translating subjects’ brain activity into commands [1, 2] They enable people with motor disabilities to interact with their environment in a completely new way [3]. This section of the population usually suffers from upper limb movement limitations and the recovery of the arm movement is often variable and incomplete [8] This recovery is crucial in order to perform activities of the daily life, so the use of BMIs during the rehabilitation may be a key factor of improvement [3]
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