Cortical Signal-Driven Kinematic Control: Implementing Human-Arm Like Movements in A 6-Dof Robotic Manipulator Via Non-Invasive BCI

Main Article Content

Ihab A. Satam
https://orcid.org/0000-0002-9749-0944
Róbert Szabolcsi

Abstract

The integration of Brain-Computer Interface (BCI) technology with robots has revolutionized assistive solutions for those with motor impairments.  Executing basic actions like gripping things or starting a handshake continues to pose a considerable problem for those with neuromuscular limitations.  In response to this difficulty, we introduce a new system that utilizes brain signals to operate robotic actuators with exceptional efficiency.  This study uses a non-invasive brain-computer interface device known as  Emotive Insight, to record neural activity and convert cognitive inputs into immediate robotic control.  The suggested system creates a seamless neuro-mechanical communication framework by analysing EEG data using Emotive software and integrating them with an Arduino-based controller.  The integration is enabled by the HITI Brain software, providing seamless interaction between cognitive intents and robotic execution.  Experimental validation, however performed on a single participant ( the main author) due to Doctoral Study graduation, has shown the system's efficacy in manipulating a six-degree-of-freedom robotic arm.  These results highlight its potential use not just in prosthetic limb control but also in wider areas, including mobility aids like wheelchairs, bicycles, and autonomous robotic systems.  This work signifies a crucial advancement in connecting human cognition with machine operation, facilitating the emergence of neuro-controlled assistive technology.


Article Details

How to Cite
abdulrahman satam, ihab, & Szabolcsi, R. (2025). Cortical Signal-Driven Kinematic Control: Implementing Human-Arm Like Movements in A 6-Dof Robotic Manipulator Via Non-Invasive BCI. Technium: Romanian Journal of Applied Sciences and Technology, 29, 84–95. https://doi.org/10.47577/technium.v29i.12807
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References

F. Gariboldi, M. Scapinello, N. Petrone, G. L. Migliore, G. Teti, and A. G. Cutti, “Static strength

of lower-limb prosthetic sockets: An exploratory study on the influence of stratigraphy, distal

adapter and lamination resin,” Med Eng Phys, vol. 114, Apr. 2023, doi:

1016/j.medengphy.2023.103970.

W. Huang, G. Yan, W. Chang, Y. Zhang, and Y. Yuan, “EEG-based classification combining

Bayesian convolutional neural networks with recurrence plot for motor movement/imagery,”

Pattern Recognit, vol. 144, Dec. 2023, doi: 10.1016/j.patcog.2023.109838.

H. Sun et al., “Feature learning framework based on EEG graph self-attention networks for motor

imagery BCI systems,” J Neurosci Methods, vol. 399, Nov. 2023, doi:

1016/j.jneumeth.2023.109969.

S. Phadikar, N. Sinha, and R. Ghosh, “Unsupervised feature extraction with autoencoders for EEG

based multiclass motor imagery BCI,” Expert Syst Appl, vol. 213, Mar. 2023, doi:

1016/j.eswa.2022.118901.

M. Nouri, F. Moradi, H. Ghaemi, and A. Motie Nasrabadi, “Towards real-world BCI: CCSPNet, a compact subject-independent motor imagery framework,” Digital Signal Processing: A Review Journal, vol. 133, Mar. 2023, doi: 10.1016/j.dsp.2022.103816.

A. Nugroho, E. M. Yuniarno, and M. H. Purnomo, “ARKOMA dataset: An open-source dataset to develop neural networks-based inverse kinematics model for NAO robot arms,” Data Brief,

vol. 51, Dec. 2023, doi: 10.1016/j.dib.2023.109727.

R. C R and D. S. C P, “BCI-AMSH: A MATLAB based open-source brain computer interface

assistive application for mental stress healing,” e-Prime - Advances in Electrical Engineering, Electronics and Energy, vol. 6, Dec. 2023, doi: 10.1016/j.prime.2023.100323.

A. Hossain, K. Das, P. Khan, and Md. F. Kader, “A BCI system for imagined Bengali speech

recognition,” Machine Learning with Applications, vol. 13, p. 100486, Sep. 2023, doi:

1016/j.mlwa.2023.100486.

K. Wang et al., “A multimodal approach to estimating vigilance in SSVEP-based BCI,” Expert

Syst Appl, vol. 225, Sep. 2023, doi: 10.1016/j.eswa.2023.120177.

S. Rimbert and S. Fleck, “Long-term kinesthetic motor imagery practice with a BCI: Impacts on

user experience, motor cortex oscillations and BCI performances,” Comput Human Behav, vol.

, Sep. 2023, doi: 10.1016/j.chb.2023.107789.

D. Delisle-Rodriguez et al., “Multi-channel EEG-based BCI using regression and classification

methods for attention training by serious game,” Biomed Signal Process Control, vol. 85, Aug.

, doi: 10.1016/j.bspc.2023.104937.

A. H. Ahmed, M. N. A. Al-Hamadani, and I. A. Satam, “Prediction of COVID-19 disease severity using machine learning techniques,” Bulletin of Electrical Engineering and Informatics, vol. 11, no. 2, pp. 1069–1074, Apr. 2022, doi: 10.11591/eei.v11i2.3272.

M. M. Fatemi and M. Manthouri, “Classification of SSVEP signals using the combined FoCCA-

KNN method and comparison with other machine learning methods,” Biomed Signal Process

Control, vol. 85, Aug. 2023, doi: 10.1016/j.bspc.2023.104957.

M. Zeynali, H. Seyedarabi, and R. Afrouzian, “Classification of EEG signals using Transformer based deep learning and ensemble models,” Biomed Signal Process Control, vol. 86, Sep. 2023,

doi: 10.1016/j.bspc.2023.105130.

J. C. Badajena, S. Sethi, and R. K. Sahoo, “Data-driven approach to designing a BCI-integrated smart wheelchair through cost–benefit analysis,” High-Confidence Computing, vol. 3, no. 2, Jun.

, doi: 10.1016/j.hcc.2023.100118.

L. Jiang et al., “SmartRolling: A human–machine interface for wheelchair control using EEG and

smart sensing techniques,” Inf Process Manag, vol. 60, no. 3, May 2023, doi:

1016/j.ipm.2022.103262.

T. Deng et al., “A VR-based BCI interactive system for UAV swarm control,” Biomed Signal

Process Control, vol. 85, Aug. 2023, doi: 10.1016/j.bspc.2023.104944.

L. Ferrero, V. Quiles, M. Ortiz, E. Iáñez, Á. Gil-Agudo, and J. M. Azorín, “Brain-computer interface enhanced by virtual reality training for controlling a lower limb exoskeleton,” iScience, vol. 26, no. 5, May 2023, doi: 10.1016/j.isci.2023.106675.

P. Soriano-Segura, M. Ortiz, E. Iáñez, and J. M. Azorín, “Design of a brain-machine interface for reducing false activations of a lower-limb exoskeleton based on error related potential,” Comput

Methods Programs Biomed, vol. 255, Oct. 2024, doi: 10.1016/j.cmpb.2024.108332.

E. Paulin et al., “A Comprehensive Review of Topography and Axon Counts in Upper-Extremity

Peripheral Nerves: A Guide for Neurotization,” J Hand Surg Glob Online, 2024, doi:

1016/j.jhsg.2024.08.002

Q. C. Kan, P. Lv, X. J. Zhang, Y. M. Xu, G. X. Zhang, and L. Zhu, “Matrine protects neuro-axon from CNS inflammation-induced injury,” Exp Mol Pathol, vol. 98, no. 1, pp. 124–130, Feb. 2015, doi: 10.1016/j.yexmp.2015.01.001.

S. Sodagudi, S. Manda, B. Smitha, N. Chaitanya, M. A. Ahmed, and N. Deb, “EEG signal

processing by feature extraction and classification based on biomedical deep learning architecture

with wireless communication,” Optik (Stuttg), vol. 270, Nov. 2022, doi:

1016/j.ijleo.2022.170037.

I. A. Satam and R. Szabolcsi, “Supervised machine learning algorithms for brain signal classification,” Military Technical Courier/Vojnotehnicki glasnik, vol. 72, no. 2, pp. 727–749, Apr. 2024, doi: 10.5937/vojtehg72-48620.