Decoding Different Reach-and-Grasp Movements Using Noninvasive Electroencephalogram

Abstract

Grasping is one of the most indispensable functions of humans. Decoding reach-and-grasp actions from electroencephalograms (EEGs) is of great significance for the realization of intuitive and natural neuroprosthesis control, and the recovery or reconstruction of hand functions of patients with motor disorders. In this paper, we investigated decoding five different reach-and-grasp movements closely related to daily life using movement-related cortical potentials (MRCPs). In the experiment, nine healthy subjects were asked to naturally execute five different reach-and-grasp movements on the designed experimental platform, namely palmar, pinch, push, twist, and plug grasp. A total of 480 trials per subject (80 trials per condition) were recorded. The MRCPs amplitude from low-frequency (0.3–3 Hz) EEG signals were used as decoding features for further offline analysis. Average binary classification accuracy for grasping vs. the no-movement condition peaked at 75.06 ± 6.8%. Peak average accuracy for grasping vs. grasping conditions of 64.95 ± 7.4% could be reached. Grand average peak accuracy of multiclassification for five grasping conditions reached 36.7 ± 6.8% at 1.45 s after the movement onset. The analysis of MRCPs indicated that all the grasping conditions are more pronounced than the no-movement condition, and there are also significant differences between the grasping conditions. These findings clearly proved the feasibility of decoding multiple reach-and-grasp actions from noninvasive EEG signals. This work is significant for the natural and intuitive BCI application, particularly for neuroprosthesis control or developing an active human–machine interaction system, such as rehabilitation robot.

Repetitive execution of a reach-and-lift task is associated with inter-trial changes in movement-related EEG features and their predictive power

“…Nonetheless, we chose a simple decoding scheme that utilizes alpha-and beta-band ERDs because this is a predictive feature that is very commonly used in many EEG-based BMIs. We also argue that these findings related to predicting rest vs. movement apply to other studies that predict more specific motor behaviors such as hand shapes 34,65,69,70 or reach direction 70,71 . The findings in this work could potentially extend to other BMI control schemes and warrant consideration of how BMIs are evaluated in terms of accuracy.…”

Electroencephalogram Source Imaging and Brain Network Based Natural Grasps Decoding

“…Moreover, they found discriminative signals originated from central motor areas based on pattern analysis. In our previous study, we investigated five natural grasp types and no-movement condition using MRCP, and five-class classification accuracy was significantly better than significance level ( Xu et al, 2021 ).…”

Coordinating human-robot collaboration by EEG-based human intention prediction and vigilance control

“…In order to predict the human's intention earlier, EEG-based methods have been investigated in previous studies (Shafiul Hasan et al, 2020 ). For example, the Bereitschafts-potential, a conspicuous deflection in the EEG signal about 500 ms before movement onset, has been used to predict when the human will move the arms and which kind of grasp action will be executed (Buerkle et al, 2021 ; Xu et al, 2021 ).…”

“…Movement-related cortical potentials, recorded by an EEG amplifier around the time of movement onset, have been shown to be informative about the upcoming grasp action, e.g., palmar, pinch, etc. (Xu et al, 2021). Motor imagery is another prominent BCI paradigm for communicating the human's intention.…”