EEG neural correlates of goal-directed movement intention

Abstract

Using low-frequency time-domain electroencephalographic (EEG) signals we show, for the same type of upper limb movement, that goal-directed movements have different neural correlates than movements without a particular goal. In a reach-and-touch task, we explored the differences in the movement-related cortical potentials (MRCPs) between goal-directed and non-goal-directed movements. We evaluated if the detection of movement intention was influenced by the goal-directedness of the movement. In a single-trial classification procedure we found that classification accuracies are enhanced if there is a goal-directed movement in mind. Furthermore, by using the classifier patterns and estimating the corresponding brain sources, we show the importance of motor areas and the additional involvement of the posterior parietal lobule in the discrimination between goal-directed movements and non-goal-directed movements. We discuss next the potential contribution of our results on goal-directed movements to a more reliable brain-computer interface (BCI) control that facilitates recovery in spinal-cord injured or stroke end-users.

Single Trial Eeg Decoding of Arm Extension and Flexion: A Case Study

“…These results are similar to some researches available in the literature [4,10,15]. The research conducted by Jochumensen et al [15] in 2015 aimed at detecting movement intention of palmar grasp with different force levels and duration.…”

“…However, in this study, the classifier was trained to identify the "relax" and "movement" screening trials, but not to differentiate movements. Pereira et al performed a research in 2017 [10] to evaluate the movement intention in different conditions: goal, no-goal, movement and no-movement tasks. The average accuracy of all tasks was 67%.…”

“…BMIs based on EEG signals have been investigated to control, for example, the beginning of movement and rest of an exoskeleton [4,8], reach [3] and grasp [9] of a robotic arm, and the movement intention for prosthesis [10,11]. The majority of these studies rely on external stimuli to the individual for executing a task in a given experiment, thus the intention of starting the movement is not endogenous.…”

“…Furthermore, BMI control systems are mainly based on the classification between execution (movement, intention of movement or motor imagery) and resting moments [4,9,10,12]. On the other hand, the differentiation of movements, such as extension and flexion of the arm, adds to the complexity of this classification task, which may impair the results.…”

Hierarchical Graphical Models for Context-Aware Hybrid Brain-Machine Interfaces

“…In the context of neurophysiologically-driven hand prosthetics, hybrid brainmachine interfaces (hBMIs) based on fusion of electroencephalographic (EEG) and electromyographic (EMG) activities to decode upper limb movements gained significant interest [1], [2]. In that regard, to investigate neural correlates of human motor behavior, a variety of recent studies have shown promising results in both EEG-based [3][4][5][6][7][8] and EMGbased [9][10][11] settings for decoding of complex same hand gestures. In the light of recent promising work, we argue that probabilistic fusion of multimodal information sources in a unified framework would yield significant insights to develop robust hybrid BMIs for neural prosthetics.…”