Neural Decoding of Upper Limb Movements Using Electroencephalography
Rationale: The human central nervous system (CNS) effortlessly performs complex hand movements with the control and coordination of multiple degrees of freedom (DoF), but how those mechanisms are encoded in the CNS remains unclear. In order to investigate
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Abstract Rationale: The human central nervous system (CNS) effortlessly performs complex hand movements with the control and coordination of multiple degrees of freedom (DoF), but how those mechanisms are encoded in the CNS remains unclear. In order to investigate the neural representations of human upper limb movement, scalp electroencephalography (EEG) was recorded to decode cortical activity in reaching and grasping movements. Methods: Upper limb movements including arm reaching and hand grasping tasks were observed in this study. EEG signals of 15 healthy individuals were recorded (g.USBamp, g.tec, Austria) when performing reaching and grasping tasks. Spectral features of the relevant cortical activities were extracted from EEG signals to decode the relevant reaching direction and hand grasping information. Upper limb motion direction and hand kinematics were captured with sensors worn on the hands. Directional EEG features were classified using stacked autoencoders; hand kinematic synergies were reconstructed to model the relationship of hand movement and EEG activities. Results: An average classification accuracy of three-direction reaching tasks achieved 79 ± 5.5% (best up to 88 ± 6%). As for hand grasp decoding, results showed that EEG features were able to successfully decode synergy-based movements with an average decoding accuracy of 80.1 ± 6.1% (best up to 93.4 ± 2.3%). Conclusion: Upper limb movements, including directional arm reaching and hand grasping expressed as weighted linear combinations of synergies, were decoded successfully using EEG. The proposed decoding and control mechanisms might simplify the complexity of high dimensional motor control and might hold promise toward real-time neural control of synergy-based prostheses and exoskeletons in the near future. Keywords Brain computer interface (BCI) · Electroencephalography (EEG) · Arm reaching movements · Hand kinematic synergies
D. Pei · M. Burns · R. Chandramouli · R. Vinjamuri (B) Sensorimotor Control Laboratory, Stevens Institute of Technology, Hoboken, NJ, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 C. Guger et al. (eds.), Brain–Computer Interface Research, SpringerBriefs in Electrical and Computer Engineering, https://doi.org/10.1007/978-3-030-49583-1_3
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Brain computer interfaces (BCIs) have become increasingly popular in recent years, bridging the gap between neural representations and external devices. Significant applications of BCI systems in motor control have been successfully accomplished [1]. Motor control aims to decode movement intention and execution encoded in multiple cortical areas, involving the integration and coordination of sensory and cognitive information. The neural signals can be decoded to trigger assistive devices to translate user intention into prosthetic control, or to drive an exoskeleton to assist and promote the user’s natural movements in rehabilitation applications [2]. A simple hand movement includes ar
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