Abstract
BackgroundDecoding neural activities associated with limb movements is the key of motor prosthesis control. So far, most of these studies have been based on invasive approaches. Nevertheless, a few researchers have decoded kinematic parameters of single hand in non-invasive ways such as magnetoencephalogram (MEG) and electroencephalogram (EEG). Regarding these EEG studies, center-out reaching tasks have been employed. Yet whether hand velocity can be decoded using EEG recorded during a self-routed drawing task is unclear.MethodsHere we collected whole-scalp EEG data of five subjects during a sequential 4-directional drawing task, and employed spatial filtering algorithms to extract the amplitude and power features of EEG in multiple frequency bands. From these features, we reconstructed hand movement velocity by Kalman filtering and a smoothing algorithm.ResultsThe average Pearson correlation coefficients between the measured and the decoded velocities are 0.37 for the horizontal dimension and 0.24 for the vertical dimension. The channels on motor, posterior parietal and occipital areas are most involved for the decoding of hand velocity. By comparing the decoding performance of the features from different frequency bands, we found that not only slow potentials in 0.1-4 Hz band but also oscillatory rhythms in 24-28 Hz band may carry the information of hand velocity.ConclusionsThese results provide another support to neural control of motor prosthesis based on EEG signals and proper decoding methods.
Highlights
Decoding neural activities associated with limb movements is the key of motor prosthesis control
From magnetoencephalogram (MEG) signals, hand movement directions have been decoded in the discrete center-out reaching task [16]; hand positions have been decoded during the continuous joystick movements [17]; and hand velocities have been decoded during the discrete center-out drawing task [18], the target-to-target joystick movements [19] and the continuous trackball movements [20]
To study the fidelity of the drawing movement decoding and the characteristics of the associated EEG signals, we will show the accuracy of the hand velocity decoding, demonstrate the scalp areas most involved for the decoding and present the frequency bands that carried information of hand velocity. 5-fold cross-validation was employed in the evaluation, i.e., each subject’s data were divided into 5 parts, among them 4 parts were used for training, and the retained part was adopted for test
Summary
Decoding neural activities associated with limb movements is the key of motor prosthesis control. A few researchers have decoded kinematic parameters of single hand in non-invasive ways such as magnetoencephalogram (MEG) and electroencephalogram (EEG) Regarding these EEG studies, center-out reaching tasks have been employed. Most progresses of these BCI systems have been based on invasive approaches using neuronal firing patterns [4,8,9], local field potentials (LFPs) [10,11] or electrocorticogram (ECoG) [12,13,14]. These signals inside head possess the advantages of little noise, high topographical resolution and broad bandwidth. The neural mechanisms of speed and tau in pointing hand movement from MEG have been revealed (tau is defined as the ratio of the current distance-to-goal gap over the current instantaneous speed towards the goal) [19]
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