The Intelligence Behind Robotic Arms: A Deep Dive into Control Evolution

Abdelrahman T. Elgohr; Mahmoud A. A. Mousa; Ahmed Reda Mohamed; Hatem A. Khater; Alfian Ma’arif; Iswanto Suwarno

doi:10.18196/jrc.v6i3.25604

Authors

Abdelrahman T. Elgohr Horus University Egypt
Mahmoud A. A. Mousa Heriot Watt University
Ahmed Reda Mohamed King Fahd University of Petroleum and Minerals (KFUPM)
Hatem A. Khater Horus University Egypt
Alfian Ma’arif Universitas Ahmad Dahlan
Iswanto Suwarno Universitas Muhammadiyah Yogyakarta

DOI:

https://doi.org/10.18196/jrc.v6i3.25604

Keywords:

Robotic Arm Control, Kinematic Analysis, Path Planning, Trajectory Optimization, Brain Computer Interface

Abstract

The intelligence behind robotic arms has evolved significantly, incorporating advanced methodologies from kinematics to brain-computer interfaces. This review critically examines the sequential steps in robotic arm control, covering Kinematic Analysis, Path Planning, Trajectory Optimization, and various Control Techniques, with a particular focus on Brain Signal Acquisition and Classification Approaches. While substantial progress has been made, key challenges persist. Traditional kinematic models often struggle with real-world uncertainties, computational inefficiencies, and singularity issues, limiting adaptability in dynamic environments. Path planning and trajectory optimization face constraints in real-time applications, where trade-offs between accuracy, computational speed, and obstacle avoidance remain critical. Control methodologies, from classical techniques to AI-driven approaches, must enhance robustness and energy efficiency to ensure stability in practical deployments. Furthermore, brain-controlled robotic arms, despite promising breakthroughs, contend with signal variability, low resolution, and the need for extensive training, raising concerns about reliability, ethical implications, and data privacy. This review consolidates recent advancements while addressing the fundamental challenges impeding seamless integration in industrial and biomedical applications. By bridging these gaps, future research can refine robotic arm intelligence, fostering more autonomous, precise, and human-integrated systems.

References

S. Kucuk and Z. Bingul, “The inverse kinematics solutions of industrial robot manipulators,” in Proceedings of the IEEE International Conference on Mechatronics, 2004. ICM ’04., IEEE, pp. 274–279, 2004, doi: 10.1109/ICMECH.2004.1364451.

L. Carbonari, M.-C. Palpacelli, and M. Callegari, “Inverse Kinematics of a Class of 6R Collaborative Robots with Non-Spherical Wrist,” Robotics, vol. 12, no. 2, 2023, doi: 10.3390/robotics12020036.

A. Pozo et al., “Vision-Driven Assembly Robot,” 2013. Accessed: Jan. 08, 2024. [Online]. Available: http://his.diva-portal.org/smash/get/diva2:1803111/FULLTEXT01.pdf

L. Bi, X.-A. Fan, and Y. Liu, “EEG-Based Brain-Controlled Mobile Robots: A Survey,” IEEE Trans Hum Mach Syst, vol. 43, no. 2, pp. 161–176, 2013, doi: 10.1109/TSMCC.2012.2219046.

M. A. Lebedev and M. A. L. Nicolelis, “Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation,” Physiol Rev, vol. 97, no. 2, pp. 767–837, 2017, doi: 10.1152/physrev.00027.2016.

J. J. Vidal, “Toward Direct Brain-Computer Communication,” Annu Rev Biophys Bioeng, vol. 2, no. 1, pp. 157–180, 1973, doi: 10.1146/annurev.bb.02.060173.001105.

J. Choi and S. Jo, “Application of Hybrid Brain-Computer Interface with Augmented Reality on Quadcopter Control,” in 2020 8th International Winter Conference on Brain-Computer Interface (BCI), pp. 1–5, 2020, doi: 10.1109/BCI48061.2020.9061659.

V. A. Knights, O. Petrovska, and J. G. Kljusurić, “Nonlinear Dynamics and Machine Learning for Robotic Control Systems in IoT Applications,” Future Internet, vol. 16, no. 12, Nov. 2024, doi: 10.3390/fi16120435.

Z. Yan et al., “Deep Learning-Driven Robot Arm Control Fusing Convolutional Visual Perception and Predictive Modeling for Motion Planning,” Journal of Organizational and End User Computing, vol. 36, no. 1, pp. 1–29, Sep. 2024, doi: 10.4018/JOEUC.355191.

Y. Dou, E. Prempain, and T. O. Ting, “Enhancing Robotic Arm Trajectory Tracking via Hybrid Weightless Swarm Algorithm and Iterative Learning Control (WSA-ILC),” in 2024 10th International Conference on Control, Decision and Information Technologies (CoDIT), IEEE, pp. 2821–2826, Jul. 2024, doi: 10.1109/CoDIT62066.2024.10708100.

S. M. Megahed, “Inverse Kinematics of Spherical Wrist Robot Arms: Analysis and Simulation,” J Intell Robot Syst, vol. 5, pp. 211–227, 1992, doi: 10.1007/BF00247418.

S. Asif and P. Webb, “Kinematics Analysis of 6-DoF Articulated Robot with Spherical Wrist,” Math Probl Eng, pp. 1–7, 2021, doi: 10.1155/2021/6647035.

M. S. Elhadidy, W. S. Abdalla, A. A. Abdelrahman, S. Elnaggar, and M. Elhosseini, “Assessing the accuracy and efficiency of kinematic analysis tools for six-DOF industrial manipulators: The KUKA robot case study,” AIMS Mathematics, vol. 9, no. 6, pp. 13944–13979, 2024, doi: 10.3934/math.2024678.

K. Raza, T. A. Khan, and N. Abbas, “Kinematic analysis and geometrical improvement of an industrial robotic arm,” Journal of King Saud University - Engineering Sciences, vol. 30, no. 3, pp. 218–223, Jul. 2018, doi: 10.1016/j.jksues.2018.03.005.

D. R. Isenberg, “Analytical Inverse Kinematic Solutions for End-Effector Positioning and Pointing Applied to Revolute Manipulators with Spherical Wrists,” in SoutheastCon 2021, IEEE, pp. 1–8, Mar. 2021, doi: 10.1109/SoutheastCon45413.2021.9401896.

L.-H. Ma, Y.-B. Zhong, G.-D. Wang, and N. Li, “The Kinematic and Dynamic Modeling and Numerical Calculation of Robots with Complex Mechanisms Based on Lie Group Theory,” Math Probl Eng, vol. 2021, pp. 1–34, Oct. 2021, doi: 10.1155/2021/6014256.

P. T. Lin, E. Shahabi, K.-A. Yang, Y.-T. Yao, and C.-H. Kuo, “Parametrically Modeled DH Table for Soft Robot Kinematics: Case Study for A Soft Gripper,” Advances in Mechanism and Machine Science: Proceedings of the 15th IFToMM World Congress on Mechanism and Machine Science 15, pp. 617–625, 2019, doi: 10.1007/978-3-030-20131-9_62.

M. A. A. Mousa, A. T. Elgohr, and H. A. Khater, “Trajectory Optimization for a 6 DOF Robotic Arm Based on Reachability Time,” Annals of Emerging Technologies in Computing, vol. 8, no. 1, pp. 22–35, Jan. 2024, doi: 10.33166/AETiC.2024.01.003.

I. Journal, T. P. Singh, P. Suresh, and S. Chandan, “Forward and Inverse Kinematic Analysis of Robotic Manipulators,” International Research Journal of Engineering and Technology (IRJET), vol. 4, no. 2, pp. 1459–1469, 2017.

R. Singh, V. Kukshal, and V. S. Yadav, “A Review on Forward and Inverse Kinematics of Classical Serial Manipulators,” in Advances in Engineering Design, pp. 417–428, 2021, doi: 10.1007/978-981-33-4018-3_39.

Z. Fu, W. Yang, and Z. Yang, “Solution of Inverse Kinematics for 6R Robot Manipulators With Offset Wrist Based on Geometric Algebra,” J Mech Robot, vol. 5, pp. 310081–310087, Jun. 2013, doi: 10.1115/1.4024239.

M. Wenz and H. Worn, “Solving the inverse kinematics problem symbolically by means of knowledge-based and linear algebra-based methods,” in IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, pp. 1346–1353, Jun. 2007, doi: 10.1109/EFTA.2007.4416937.

I. Zaplana, H. Hadfield, and J. Lasenby, “Closed-form solutions for the inverse kinematics of serial robots using conformal geometric algebra,” Mech Mach Theory, vol. 173, p. 104835, 2022, doi: 10.1016/j.mechmachtheory.2022.104835.

K. Arikawa, “Symbolic Computation of Inverse Kinematics for General 6R Manipulators Based on Raghavan and Roth’s Solution,” in Volume 10: 44th Mechanisms and Robotics Conference (MR), American Society of Mechanical Engineers, Aug. 2020, doi: 10.1115/DETC2020-22231.

H. Shen, Y. Zhao, J. Li, G. Wu, and D. Chablat, “A novel partially-decoupled translational parallel manipulator with symbolic kinematics, singularity identification and workspace determination,” Mech Mach Theory, vol. 164, p. 104388, Oct. 2021, doi: 10.1016/j.mechmachtheory.2021.104388.

A. A. Saad, “An Efficient Full-Numerical Solution for The Inverse Kinematics of The 6-DOF Full-Articulated Manipulator,” The International Conference on Applied Mechanics and Mechanical Engineering, vol. 18, pp. 3–5, 2018.

R. Iakovlev, A. Denisov, and R. Prakapovich, “Iterative Method for Solving the Inverse Kinematics Problem of Multi-link Robotic Systems with Rotational Joints,” Proceedings of 14th International Conference on Electromechanics and Robotics “Zavalishin's Readings” ER (ZR) 2019, pp. 237–251, 2019, doi: 10.1007/978-981-13-9267-2_20.

Y. Quiñonez, O. Zatarain, C. Lizarraga, R. Aguayo, and J. Mejía, “Numerical Method Using Homotopic Iterative Functions Based on the via Point for the Joint-Space Trajectory Generation,” Applied Sciences, vol. 13, no. 2, Jan. 2023, doi: 10.3390/app13021142.

S. Horvath and H. Neuner, “Introduction of a Framework for the Integration of a Kinematic Robot Arm Model in an Artificial Neural Network - Extended Kalman Filter Approach,” J Intell Robot Syst, vol. 110, no. 4, p. 137, Sep. 2024, doi: 10.1007/s10846-024-02164-6.

Y. Hamida El Naser, D. Karayel, M. S. Demirsoy, M. S. Sarıkaya, and N. Y. Peker, “Robotic Arm Trajectory Tracking Using Image Processing and Kinematic Equations,” Black Sea Journal of Engineering and Science, vol. 7, no. 3, pp. 436–444, May 2024, doi: 10.34248/bsengineering.1445455.

M. Restrepo, C. A. Cañizares, J. W. Simpson-Porco, P. Su, and J. Taruc, “Optimization- and Rule-based Energy Management Systems at the Canadian Renewable Energy Laboratory microgrid facility,” Appl Energy, vol. 290, May 2021, doi: 10.1016/j.apenergy.2021.116760.

N. Gunantara, “A review of multi-objective optimization: Methods and its applications,” Cogent Eng, vol. 5, no. 1, p. 1502242, Jan. 2018, doi: 10.1080/23311916.2018.1502242.

A. B. Hashemi and M. R. Meybodi, “Cellular PSO: A PSO for Dynamic Environments,” Advances in Computation and Intelligence: 4th International Symposium, ISICA 2009 Huangshi, China, Ocotober 23-25, 2009 Proceedings 4, pp. 422–433, 2009, doi: 10.1007/978-3-642-04843-2_45.

M. Alkayyali and T. A. Tutunji, “PSO-based Algorithm for Inverse Kinematics Solution of Robotic Arm Manipulators,” 2019 20th International Conference on Research and Education in Mechatronics (REM), pp. 1–6, 2019.

A. Singh, A. Sharma, S. Rajput, A. Bose, and X. Hu, “An Investigation on Hybrid Particle Swarm Optimization Algorithms for Parameter Optimization of PV Cells,” Electronics (Basel), vol. 11, no. 6, Mar. 2022, doi: 10.3390/electronics11060909.

H. Deng and C. Xie, “An Improved Particle Swarm Optimization Algorithm for Inverse Kinematics Solution of Multi-DOF Serial Robotic Manipulators,” Soft Comput., vol. 25, no. 21, pp. 13695–13708, Nov. 2021, doi: 10.1007/s00500-021-06007-6.

H. Danaci, L. A. Nguyen, T. L. Harman, and M. Pagan, “Inverse Kinematics for Serial Robot Manipulators by Particle Swarm Optimization and POSIX Threads Implementation,” Applied Sciences, vol. 13, no. 7, Apr. 2023, doi: 10.3390/app13074515.

M. Kumar, M. Husian, N. Upreti, and D. Gupta, “Genetic Algorithm: Review and Application,” International Journal of Information Technology and Knowledge Management, vol. 2, no. 2, pp. 451–454, 2010, doi: 10.2139/ssrn.3529843.

C.-Y. Chen, M.-G. Her, Y.-C. Hung, and M. Karkoub, “Approximating a Robot Inverse Kinematics Solution Using Fuzzy Logic Tuned by Genetic Algorithms,” The International Journal of Advanced Manufacturing Technology, vol. 20, no. 5, pp. 375–380, Sep. 2002, doi: 10.1007/s001700200166.

R. Köker, “A genetic algorithm approach to a neural-network-based inverse kinematics solution of robotic manipulators based on error minimization,” Inf Sci (N Y), vol. 222, pp. 528–543, 2013, doi: 10.1016/j.ins.2012.07.051.

Z. Zhou, H. Guo, Y. Wang, Z. Zhu, J. Wu, and X. Liu, “Inverse kinematics solution for robotic manipulator based on extreme learning machine and sequential mutation genetic algorithm,” Int J Adv Robot Syst, vol. 15, no. 4, Jul. 2018, doi: 10.1177/1729881418792992.

J. Hernandez-Barragan, G. Martinez-Soltero, J. D. Rios, C. Lopez-Franco, and A. Y. Alanis, “A Metaheuristic Optimization Approach to Solve Inverse Kinematics of Mobile Dual-Arm Robots,” Mathematics, vol. 10, no. 21, Nov. 2022, doi: 10.3390/math10214135.

N. Courty and E. Arnaud, “Inverse Kinematics Using Sequential Monte Carlo Methods,” in Perales, F.J., Fisher, R.B. (eds) Articulated Motion and Deformable Objects. AMDO, Jun. 2008, doi: 10.1007/978-3-540-70517-8_1.

A. Martín-Barrio, A. Barrientos, and J. Cerro, “The Natural-CCD Algorithm, a Novel Method to Solve the Inverse Kinematics of Hyper-redundant and Soft Robots,” Soft Robot, vol. 5, Jun. 2018, doi: 10.1089/soro.2017.0009.

M. Soleimani Amiri and R. Ramli, “Intelligent Trajectory Tracking Behavior of a Multi-Joint Robotic Arm via Genetic–Swarm Optimization for the Inverse Kinematic Solution,” Sensors, vol. 21, no. 9, May 2021, doi: 10.3390/s21093171.

A. Sheelwant, P. M. Jadhav, and S. K. R. Narala, “ANN-GA based parametric optimization of Al-TiB2 metal matrix composite material processing technique,” Mater Today Commun, vol. 27, Jun. 2021, doi: 10.1016/j.mtcomm.2021.102444.

A.-V. Duka, “Neural Network based Inverse Kinematics Solution for Trajectory Tracking of a Robotic Arm,” Procedia Technology, vol. 12, pp. 20–27, 2014, doi: 10.1016/j.protcy.2013.12.451.

M. Soylak, T. Oktay, and İ. Turkmen, “A simulation-based method using artificial neural networks for solving the inverse kinematic problem of articulated robots,” Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, vol. 231, no. 3, pp. 470–479, 2017, doi: 10.1177/0954408915608755.

J. Lu, T. Zou, and X. Jiang, “A Neural Network Based Approach to Inverse Kinematics Problem for General Six-Axis Robots,” Sensors, vol. 22, no. 22, p. 8909, Nov. 2022, doi: 10.3390/s22228909.

M. R. A. Refaai, “An Improved Inverse Kinematics Solution for a Robot Arm Trajectory Using Multiple Adaptive Neuro-Fuzzy Inference Systems,” Advances in Materials Science and Engineering, vol. 2023, 2022, doi: 10.1155/2022/1413952.

J. Narayan and A. Singla, “ANFIS based kinematic analysis of a 4-DOFs SCARA robot,” in 2017 4th International Conference on Signal Processing, Computing and Control (ISPCC), IEEE, pp. 205–211, Sep. 2017, doi: 10.1109/ISPCC.2017.8269676.

D. Deshmukh, D. K. Pratihar, A. K. Deb, H. Ray, and A. Ghosh, “ANFIS-Based Inverse Kinematics and Forward Dynamics of 3 DOF Serial Manipulator,” in Hybrid Intelligent Systems. HIS, vol. 1375, pp. 144–156, 2021, doi: 10.1007/978-3-030-73050-5_15.

J. Demby’s, Y. Gao, and G. N. DeSouza, “A Study on Solving the Inverse Kinematics of Serial Robots using Artificial Neural Network and Fuzzy Neural Network,” in 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, pp. 1–6, Jun. 2019, doi: 10.1109/FUZZ-IEEE.2019.8858872.

R. Singh, Ankur, A. Agrawal, A. Mishra, P. K. Arya, and A. Sharma, “Application of Deep Learning Model for Analysis of Forward Kinematics of a 6-Axis Robotic Hand for a Humanoid,” in 2024 3rd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS), IEEE, pp. 1–6, Jul. 2024, doi: 10.1109/AIARS63200.2024.00006.

M. Kashkash, A. El Saddik, and M. Guizani, “Optimizing 7-DOF Robot Manipulator Path Using Deep Reinforcement Learning Techniques,” in 2024 IEEE International Symposium on Robotic and Sensors Environments (ROSE), IEEE, pp. 1–7, Jun. 2024, doi: 10.1109/ROSE62198.2024.10591186.

A. Serat and B. Djebbar, “Integrating artificial immune systems and multi-layer perceptron-biogeography-based optimization for enhanced inverse kinematics in robotic arm,” IAES International Journal of Robotics and Automation (IJRA), vol. 13, no. 4, pp. 401–409, Dec. 2024, doi: 10.11591/ijra.v13i4.pp401-409.

O. Aydogmus and G. Boztas, “Implementation of singularity-free inverse kinematics for humanoid robotic arm using Bayesian optimized deep neural network,” Measurement, vol. 229, Apr. 2024, doi: 10.1016/j.measurement.2024.114471.

W. Ma, Q. Du, R. Zhu, W. Han, D. Chen, and Y. Geng, “Research on Inverse Kinematics of Redundant Robotic Arms Based on Flexibility Index,” IEEE Robot Autom Lett, vol. 9, no. 8, pp. 7262–7269, Aug. 2024, doi: 10.1109/LRA.2024.3420704.

M. Fazilat, N. Zioui, and J. St-Arnaud, “A novel quantum model of forward kinematics based on quaternion/Pauli gate equivalence: Application to a six-jointed industrial robotic arm,” Results in Engineering, vol. 14, Jun. 2022, doi: 10.1016/j.rineng.2022.100402.

A. Gasparetto, P. Boscariol, A. Lanzutti, and R. Vidoni, “Path Planning and Trajectory Planning Algorithms: A General Overview,” Mechanisms and Machine Science, vol. 29, pp. 3–27, Mar. 2015, doi: 10.1007/978-3-319-14705-5_1.

M. Ali and M. Mailah, “Path Planning and Control of Mobile Robot in Road Environments Using Sensor Fusion and Active Force Control,” IEEE Trans Veh Technol, vol. 68, no. 3, pp. 2176-2195, Jan. 2019, doi: 10.1109/TVT.2019.2893878.

J. Yi, Q. Yuan, R. Sun, and H. Bai, “Path planning of a manipulator based on an improved P_RRT* algorithm,” Complex & Intelligent Systems, vol. 8, no. 3, pp. 2227–2245, 2022, doi: 10.1007/s40747-021-00628-y.

J.-Y. Kim, U.-J. Yang, and K. Park, “Design, motion planning and control of frozen shoulder rehabilitation robot,” International Journal of Precision Engineering and Manufacturing, vol. 15, no. 9, pp. 1875–1881, 2014, doi: 10.1007/s12541-014-0541-4.

R. Soltani Zarrin, A. Zeiaee, R. Langari, and N. Robson, “Reference path generation for upper-arm exoskeletons considering scapulohumeral rhythms,” in 2017 International Conference on Rehabilitation Robotics (ICORR), pp. 753–758, 2017, doi: 10.1109/ICORR.2017.8009338.

Q. Meng, H. Shao, L. Wang, and H. Yu, “Task-Based Trajectory Planning for an Exoskeleton Upper Limb Rehabilitation Robot,” International Conference on Man-Machine-Environment System Engineering (MMESE), vol. 527, pp. 141–149, Jan. 2019, doi: 10.1007/978-981-13-2481-9_18.

C. Wang et al., “Kinematic Redundancy Analysis during Goal-Directed Motion for Trajectory Planning of an Upper-Limb Exoskeleton Robot,” in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5251–5255, 2019, doi: 10.1109/EMBC.2019.8857716.

G. Li, Q. Fang, T. Xu, J. Zhao, H. Cai, and Y. Zhu, “Inverse kinematic analysis and trajectory planning of a modular upper limb rehabilitation exoskeleton,” Technology and Health Care, vol. 27, pp. 123–132, 2019, doi: 10.3233/THC-199012.

Z. Li, W. Zuo, and S. Li, “Zeroing dynamics method for motion control of industrial upper-limb exoskeleton system with minimal potential energy modulation,” Measurement, vol. 163, p. 107964, 2020, doi: https://doi.org/10.1016/j.measurement.2020.107964.

M. A. A. Mousa, A. Elgohr, and H. Khater, “Path Planning for a 6 DoF Robotic Arm Based on Whale Optimization Algorithm and Genetic Algorithm,” Journal of Engineering Research, vol. 7, no. 5, pp. 160–168, Nov. 2023, doi: 10.21608/erjeng.2023.237586.1256.

R. K. Malhan, S. Thakar, A. M. Kabir, P. Rajendran, P. M. Bhatt, and S. K. Gupta, “Generation of Configuration Space Trajectories Over Semi-Constrained Cartesian Paths for Robotic Manipulators,” IEEE Transactions on Automation Science and Engineering, vol. 20, no. 1, pp. 193–205, Jan. 2023, doi: 10.1109/TASE.2022.3144673.

J.-C. Chung, C.-H. Huang, H.-C. Chou, and C.-H. Kuo, “Trajectory Planning of a Redundant Manipulator from Investigations of Upper Limb Motions of Human Beings,” IFAC Proceedings Volumes, vol. 45, no. 2, pp. 538–543, 2012.

E. Sabbaghi, M. Bahrami, and S. S. Ghidary, “Learning of gestures by imitation using a monocular vision system on a humanoid robot,” 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM), pp. 588–594, 2014, doi: 10.1109/ICRoM.2014.6990966.

J. Garrido, W. Yu, and X. Li, “Robot trajectory generation using modified hidden Markov model and Lloyd’s algorithm in joint space,” Eng Appl Artif Intell, vol. 53, pp. 32–40, Aug. 2016, doi: 10.1016/j.engappai.2016.03.006.

C. Lauretti et al., “Learning by Demonstration for Motion Planning of Upper-Limb Exoskeletons,” Front Neurorobot, vol. 12, 2018, doi: 10.3389/fnbot.2018.00005.

S. S. Naghibi, A. Fallah, A. Maleki, and F. Ghassemi, “Elbow angle generation during activities of daily living using a submovement prediction model,” Biol Cybern, vol. 114, no. 3, pp. 389–402, 2020.

F. Tao et al., “Trajectory Planning of Upper Limb Rehabilitation Robot Based on Human Pose Estimation,” 2020 17th International Conference on Ubiquitous Robots (UR), pp. 333–338, Jun. 2020, doi: 10.1109/UR49135.2020.9144771.

A. Duburcq, Y. Chevaleyre, N. Bredeche, and G. Boeris, “Online Trajectory Planning Through Combined Trajectory Optimization and Function Approximation: Application to the Exoskeleton Atalante,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 3756–3762, 2020, doi: 10.1109/ICRA40945.2020.9196633.

S. Zhang, Q. Xia, M. Chen, and S. Cheng, “Multi-Objective Optimal Trajectory Planning for Robotic Arms Using Deep Reinforcement Learning,” Sensors, vol. 23, no. 13, Jun. 2023.

A. Gasparetto, P. Boscariol, A. Lanzutti, and R. Vidoni, “Trajectory Planning in Robotics,” Mathematics in Computer Science, vol. 6, no. 3, pp. 269–279, Sep. 2012, doi: 10.1007/s11786-012-0123-8.

F. G. Flores and A. Kecskeméthy, “Time-Optimal Path Planning Along Specified Trajectories,” in Multibody System Dynamics, Robotics and Control, H. Gattringer and J. Gerstmayr, Eds., Vienna: Springer Vienna, pp. 1–16, 2013, doi: 10.1007/978-3-7091-1289-2_1.

H. Liu, X. Chen, and C. Li, “Path Planning Optimization for Obstacle Avoidance in Unknown Environment,” in 2024 36th Chinese Control and Decision Conference (CCDC), pp. 5601–5606, 2024, doi: 10.1109/CCDC62350.2024.10588054.

A. Gasparetto and V. Zanotto, “A new method for smooth trajectory planning of robot manipulators,” Mech Mach Theory, vol. 42, no. 4, pp. 455–471, 2007, doi: 10.1016/j.mechmachtheory.2006.04.002.

M. Ratiu and M. Adriana Prichici, “Industrial robot trajectory optimization- a review,” in MATEC Web of Conferences, EDP Sciences, Oct. 2017. doi: 10.1051/matecconf/201712602005.

Y. Chen, L. Chen, and J. Ding, “Adaptive Genetic Algorithm Based Particle Swarm Optimization for Industrial Robotic Arm Obstacle Avoidance Trajectory Optimization,” in 2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE), pp. 1–4, 2022, doi: 10.1109/ICARCE55724.2022.10046592.

Y. Su, Y. Dai, and Y. Liu, “A hybrid hyper-heuristic whale optimization algorithm for reusable launch vehicle reentry trajectory optimization,” Aerosp Sci Technol, vol. 119, p. 107200, Dec. 2021, doi: 10.1016/j.ast.2021.107200.

A. Heim and O. Von Stryk, “Trajectory optimization of industrial robots with application to computer-aided robotics and robot controllers,” Optimization, vol. 47, no. 3–4, pp. 407–420, Jan. 2000.

C. Rösmann, W. Feiten, T. Wösch, F. Hoffmann, and T. Bertram, “Efficient trajectory optimization using a sparse model,” in 2013 European Conference on Mobile Robots, pp. 138–143, 2013, doi: 10.1109/ECMR.2013.6698833.

G. Kahn et al., “Active exploration using trajectory optimization for robotic grasping in the presence of occlusions,” in 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 4783–4790, 2015, doi: 10.1109/ICRA.2015.7139864.

R. Benotsmane, L. Dudás, and G. Kovács, “Trajectory Optimization of Industrial Robot Arms Using a Newly Elaborated ‘Whip-Lashing’ Method,” Applied Sciences, vol. 10, no. 23, p. 8666, Dec. 2020, doi: 10.3390/app10238666.

T. Wang, S. Yao, and S. Zhu, “Energy-saving trajectory optimization of a fluidic soft robotic arm,” Smart Mater Struct, vol. 31, no. 11, p. 115011, 2022, doi: 10.1088/1361-665X/ac9768.

N. Yokoyama and S. Suzuki, “Modified genetic algorithm for constrained trajectory optimization,” Journal of Guidance, Control, and Dynamics, vol. 28, no. 1, pp. 139–144, 2005, doi: 10.2514/1.3042.

S. Števo, I. Sekaj, and M. Dekan, “Optimization of Robotic Arm Trajectory Using Genetic Algorithm,” 19th IFAC World Congress, vol. 47, no. 3, pp. 1748–1753, 2014, doi: 10.3182/20140824-6-ZA-1003.01073.

R. Wu, Y. Yang, X. Yao, and N. Lu, “Optimal Trajectory Planning for Robotic Arm Based on Improved Dynamic Multi-Population Particle Swarm Optimization Algorithm,” International Journal of Advanced Computer Science and Applications, vol. 15, no. 5, 2024, doi: 10.14569/IJACSA.2024.0150571.

M. A. A. Mousa, A. T. Elgohr, and H. A. Khater, “Whale-Based Trajectory Optimization Algorithm for 6 DOF Robotic Arm,” Annals of Emerging Technologies in Computing (AETiC), vol. 8, no. 4, pp. 99–114, Oct. 2024, doi: 10.33166/AETiC.2024.04.005.

T. Wang, Z. Xin, H. Miao, H. Zhang, Z. Chen, and Y. Du, “Optimal Trajectory Planning of Grinding Robot Based on Improved Whale Optimization Algorithm,” Math Probl Eng, vol. 2020, p. 3424313, 2020, doi: 10.1155/2020/3424313.

Y. Dai, J. Yu, C. Zhang, B. Zhan, and X. Zheng, “A novel whale optimization algorithm of path planning strategy for mobile robots,” Applied Intelligence, vol. 53, no. 9, pp. 10843–10857, 2023, doi: 10.1007/s10489-022-04030-0.

S. Ziamanesh, H. S. Salimi, A. Tavana, and A. A. Ghavifekr, “Parameter Tuning of Discontinues Lyapunov Based Controller Based on the Gray Wolf Optimization Algorithm Applied to a Robotic Manipulator,” in 2022 8th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), pp. 1–6, 2022, doi: 10.1109/ICSPIS56952.2022.10043942.

A. Pawlowski, S. Romaniuk, Z. Kulesza, and M. Petrovic, “Trajectory optimization using learning from demonstration with meta-heuristic grey wolf algorithm,” IAES International Journal of Robotics and Automation (IJRA), vol. 11, no. 4, pp. 263–277, Dec. 2022, doi: 10.11591/ijra.v11i4.pp263-277.

Z. Wang, X. Zhou, C. Xu, and F. Gao, “Geometrically Constrained Trajectory Optimization for Multicopters,” IEEE Transactions on Robotics, vol. 38, no. 5, pp. 3259–3278, Oct. 2022, doi: 10.1109/TRO.2022.3160022.

A. Dastider, H. Fang, and M. Lin, “RETRO: Reactive Trajectory Optimization for Real-Time Robot Motion Planning in Dynamic Environments,” in 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 8764–8770, 2024, doi: 10.1109/ICRA57147.2024.10610542.

K. Wei, Y. Chu, and H. Gan, “An improved Rapidly-exploring Random Tree Approach for Robotic Dynamic Path Planning,” in 2021 11th International Conference on Intelligent Control and Information Processing (ICICIP), pp. 181–187, 2021, doi: 10.1109/ICICIP53388.2021.9642182.

Sam Cubero, Industrial Robotics: Theory, Modelling and Control. Russia: Pro Literatur Verlag, Germany / ARS, Austria, 2006. doi: 10.5772/44.

Q. V. Doan, A. T. Vo, T. D. Le, H.-J. Kang, and N. H. A. Nguyen, “A Novel Fast Terminal Sliding Mode Tracking Control Methodology for Robot Manipulators,” Applied Sciences, vol. 10, no. 9, p. 3010, Apr. 2020, doi: 10.3390/app10093010.

B. House, J. Malkin, and J. Bilmes, “The VoiceBot: A Voice Controlled Robot Arm,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, in CHI ’09. New York, NY, USA: Association for Computing Machinery, pp. 183–192, 2009, doi: 10.1145/1518701.1518731.

K. Zinchenko, C.-Y. Wu, and K.-T. Song, “A Study on Speech Recognition Control for a Surgical Robot,” IEEE Trans Industr Inform, vol. 13, no. 2, pp. 607–615, 2017, doi: 10.1109/TII.2016.2625818.

K. Gundogdu, S. Bayrakdar, and I. Yucedag, “Developing and modeling of voice control system for prosthetic robot arm in medical systems,” Journal of King Saud University - Computer and Information Sciences, vol. 30, no. 2, pp. 198–205, 2018, doi: 10.1016/j.jksuci.2017.04.005.

T. B. Pulikottil, M. Caimmi, M. G. D’Angelo, E. Biffi, S. Pellegrinelli, and L. M. Tosatti, “A Voice Control System for Assistive Robotic Arms: Preliminary Usability Tests on Patients,” in 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), pp. 167–172, 2018, doi: 10.1109/BIOROB.2018.8487200.

S. Yuvaraj, A. Badholia, P. William, K. Vengatesan, and R. Bibave, “Speech recognition based robotic arm writing,” in Proceedings of International Conference on Communication and Artificial Intelligence: ICCAI 2021, pp. 23-33, 2022.

H. Jiang, J. P. Wachs, and B. S. Duerstock, “Integrated vision-based robotic arm interface for operators with upper limb mobility impairments,” in 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR), pp. 1–6, 2013, doi: 10.1109/ICORR.2013.6650447.

Z. Wang et al., “Vision-Based Calibration of Dual RCM-Based Robot Arms in Human-Robot Collaborative Minimally Invasive Surgery,” IEEE Robot Autom Lett, vol. 3, no. 2, pp. 672–679, 2018, doi: 10.1109/LRA.2017.2737485.

A. Noman, A. Eva, T. Yeahyea, and R. Khan, “Computer Vision-based Robotic Arm for Object Color, Shape, and Size Detection,” Journal of Robotics and Control (JRC), vol. 3, pp. 180–186, Jul. 2022, doi: 10.18196/jrc.v3i2.13906.

A. Kupferberg, S. Glasauer, M. Huber, M. Rickert, A. Knoll, and T. Brandt, “Biological movement increases acceptance of humanoid robots as human partners in motor interaction,” AI Soc, vol. 26, no. 4, pp. 339–345, 2011, doi: 10.1007/s00146-010-0314-2.

A. Sciutti et al., “Measuring Human-Robot Interaction Through Motor Resonance,” Int J Soc Robot, vol. 4, no. 3, pp. 223–234, 2012.

L. Psarakis, D. Nathanael, and N. Marmaras, “Fostering short-term human anticipatory behavior in human-robot collaboration,” Int J Ind Ergon, vol. 87, p. 103241, 2022, doi: 10.1016/j.ergon.2021.103241.

P. Zhou et al., “Reactive human–robot collaborative manipulation of deformable linear objects using a new topological latent control model,” Robot Comput Integr Manuf, vol. 88, Aug. 2024, doi: 10.1016/j.rcim.2024.102727.

M. A. A. Mousa, A. T. Elgohr, and H. A. Khater, “A Novel Hybrid Deep Neural Network Classifier for EEG Emotional Brain Signals,” International Journal of Advanced Computer Science and Applications, vol. 15, no. 6, 2024, doi: 10.14569/IJACSA.2024.01506107.

V. Straebel and W. Thoben, “Alvin Lucier’s Music for Solo Performer : Experimental music beyond sonification,” Organised Sound, vol. 19, no. 1, pp. 17–29, Apr. 2014.

J. J. Vidal, “Real-time detection of brain events in EEG,” Proceedings of the IEEE, vol. 65, no. 5, pp. 633–641, 1977, doi: 10.1109/PROC.1977.10542.

J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan, “Brain–computer interfaces for communication and control,” Clinical Neurophysiology, vol. 113, no. 6, pp. 767–791, 2002, doi: 10.1016/S1388-2457(02)00057-3.

B. Z. Allison, E. W. Wolpaw, and J. R. Wolpaw, “Brain–computer interface systems: progress and prospects,” Expert Rev Med Devices, vol. 4, no. 4, pp. 463–474, 2007, doi: 10.1586/17434440.4.4.463.

S. Bozinovski and L. Bozinovska, “Brain–Computer Interface in Europe: the thirtieth anniversary,” Automatika, vol. 60, no. 1, pp. 36–47, 2019, doi: 10.1080/00051144.2019.1570644.

S. Bozinovski, “Controlling Robots Using EEG Signals, Since 1988,” in ICT Innovations 2012, M. Markovski Smile and Gusev, Ed., Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 1–11, 2013.

J. Cao, S. Q. Xie, R. Das, and G. L. Zhu, “Control strategies for effective robot assisted gait rehabilitation: The state of art and future prospects,” Med Eng Phys, vol. 36, no. 12, pp. 1555–1566, Dec. 2014, doi: 10.1016/j.medengphy.2014.08.005.

M. Lebedev, “Augmentation of Sensorimotor Functions with Neural Prostheses,” Opera Med Physiol, vol. 2, no. 3, pp. 211–227, 2016, doi: 10.20388/OMP.003.0035.

R. A. Miranda et al., “DARPA-funded efforts in the development of novel brain–computer interface technologies,” J Neurosci Methods, vol. 244, pp. 52–67, 2015, doi: 10.1016/j.jneumeth.2014.07.019.

M. Jacobs, A. Premji, and A. J. Nelson, “Plasticity-Inducing TMS Protocols to Investigate Somatosensory Control of Hand Function,” Neural Plast, vol. 2012, p. 350574, 2012, doi: 10.1155/2012/350574.

M. Fox, “Using Brain Machine Interface in Medicine and Comparative Analysis of EEG and ECoG,” International Research Journal of Modernization in Engineering Technology and Science, vol. 4, no. 10, pp. 216–223, Oct. 2022, doi: 10.56726/irjmets30428.

A. T. Elgohr, M. S. Elhadidy, M. Elazab, R. Ahmed Hegazii, and M. M. El Sherbiny, “Multi-Classification Model for Brain Tumor Early Prediction Based on Deep Learning Techniques,” Journal of Engineering Research, vol. 8, p. 2024, 2024.

D. W. Moran and A. B. Schwartz, “Motor Cortical Representation of Speed and Direction During Reaching,” J Neurophysiol, vol. 82, no. 5, pp. 2676–2692, Nov. 1999, doi: 10.1152/jn.1999.82.5.2676.

M. Velliste, S. Perel, M. C. Spalding, A. S. Whitford, and A. B. Schwartz, “Cortical control of a prosthetic arm for self-feeding,” Nature, vol. 453, no. 7198, pp. 1098–1101, 2008, doi: 10.1038/nature06996.

L. R. Hochberg et al., “Reach and grasp by people with tetraplegia using a neurally controlled robotic arm,” Nature, vol. 485, no. 7398, pp. 372–375, 2012, doi: 10.1038/nature11076.

J. L. Collinger et al., “High-performance neuroprosthetic control by an individual with tetraplegia,” The Lancet, vol. 381, no. 9866, pp. 557–564, Feb. 2013, doi: 10.1016/S0140-6736(12)61816-9.

C. E. Bouton et al., “Restoring cortical control of functional movement in a human with quadriplegia,” Nature, vol. 533, no. 7602, pp. 247–250, 2016, doi: 10.1038/nature17435.

C. Márquez-Chin, M. R. Popovic, T. Cameron, A. M. Lozano, and R. Chen, “Control of a neuroprosthesis for grasping using off-line classification of electrocorticographic signals: case study,” Spinal Cord, vol. 47, no. 11, pp. 802–808, 2009, doi: 10.1038/sc.2009.41.

M. Hirata and T. Yoshimine, “Electrocorticographic Brain–Machine Interfaces for Motor and Communication Control,” in Clinical Systems Neuroscience, pp. 83–100, 2015, doi: 10.1007/978-4-431-55037-2_5.

H.-H. Kim and J. Jeong, “An electrocorticographic decoder for arm movement for brain–machine interface using an echo state network and Gaussian readout,” Appl Soft Comput, vol. 117, p. 108393, 2022, doi: https://doi.org/10.1016/j.asoc.2021.108393.

H. A. Shedeed, M. F. Issa, and S. M. El-sayed, “Brain EEG signal processing for controlling a robotic arm,” in 2013 8th International Conference on Computer Engineering & Systems (ICCES), pp. 152–157, 2013, doi: 10.1109/ICCES.2013.6707191.

J. Meng, S. Zhang, A. Bekyo, J. Olsoe, B. Baxter, and B. He, “Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks,” Sci Rep, vol. 6, no. 1, p. 38565, 2016, doi: 10.1038/srep38565.

R. Bousseta, I. El Ouakouak, M. Gharbi, and F. Regragui, “EEG Based Brain Computer Interface for Controlling a Robot Arm Movement Through Thought,” IRBM, vol. 39, no. 2, pp. 129–135, 2018, doi: 10.1016/j.irbm.2018.02.001.

J. H. Jeong, K. H. Shim, D. J. Kim, and S. W. Lee, “Brain-Controlled Robotic Arm System Based on Multi-Directional CNN-BiLSTM Network Using EEG Signals,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 5, pp. 1226–1238, May 2020, doi: 10.1109/TNSRE.2020.2981659.

Z. Miao, M. Zhao, X. Zhang, and D. Ming, “LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interfaces and interpretability,” Neuroimage, vol. 276, Aug. 2023, doi: 10.1016/j.neuroimage.2023.120209.

S. M. Coyle, T. E. Ward, and C. M. Markham, “Brain–computer interface using a simplified functional near-infrared spectroscopy system,” J Neural Eng, vol. 4, no. 3, p. 219, 2007.

C. Canning and M. Scheutz, “Functional Near-Infrared Spectroscopy in Human-Robot Interaction,” J. Hum.-Robot Interact., vol. 2, no. 3, pp. 62–84, Sep. 2013, doi: 10.5898/JHRI.2.3.Canning.

S. Zhang et al., “Application of a common spatial pattern-based algorithm for an fNIRS-based motor imagery brain‐computer interface,” Neurosci Lett, vol. 655, pp. 35–40, 2017, doi: 10.1016/j.neulet.2017.06.044.

S.-S. Yoo et al., “Brain–computer interface using fMRI: spatial navigation by thoughts,” Neuroreport, vol. 15, no. 10, pp. 1591–1595, 2004, doi: 10.1097/01.wnr.0000133296.39160.fe.

L. Minati, A. Nigri, C. Rosazza, and M. G. Bruzzone, “Thoughts turned into high-level commands: Proof-of-concept study of a vision-guided robot arm driven by functional MRI (fMRI) signals,” Med Eng Phys, vol. 34, no. 5, pp. 650–658, 2012, doi: 10.1016/j.medengphy.2012.02.004.

O. Cohen et al., “fMRI-Based Robotic Embodiment: Controlling a Humanoid Robot by Thought Using Real-Time fMRI,” Presence, vol. 23, no. 3, pp. 229–241, 2014, doi: 10.1162/PRES_a_00191.

W. McClay, N. Yadav, Y. Ozbek, A. Haas, H. Attias, and S. Nagarajan, “A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D Visualization and the Hadoop Ecosystem,” Brain Sci, vol. 5, no. 4, pp. 419–440, Sep. 2015, doi: 10.3390/brainsci5040419.

R. Fukuma et al., “Real-Time Control of a Neuroprosthetic Hand by Magnetoencephalographic Signals from Paralysed Patients,” Sci Rep, vol. 6, no. 1, p. 21781, 2016, doi: 10.1038/srep21781.

D. Rathee, H. Raza, S. Roy, and G. Prasad, “A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface,” Sci Data, vol. 8, no. 1, p. 120, 2021, doi: 10.1038/s41597-021-00899-7.

A. Malki, C. Yang, N. Wang, and Z. Li, “Mind guided motion control of robot manipulator using EEG signals,” in 2015 5th International Conference on Information Science and Technology (ICIST), pp. 553–558, 2015, doi: 10.1109/ICIST.2015.7289033.

S. Bhattacharyya, A. Konar, and D. N. Tibarewala, “Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose,” Med Biol Eng Comput, vol. 52, no. 12, pp. 1007–1017, 2014, doi: 10.1007/s11517-014-1204-4.

D. H. Krishna, I. A. Pasha, and T. S. Savithri, “Autonomuos robot control based on EEG and cross-correlation,” in 2016 10th International Conference on Intelligent Systems and Control (ISCO), pp. 1–4, 2016, doi: 10.1109/ISCO.2016.7727098.

I. Sakamaki, C. E. P. del Campo, S. A. Wiebe, M. Tavakoli, and K. Adams, “Assistive technology design and preliminary testing of a robot platform based on movement intention using low-cost brain computer interface,” in 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2243–2248, 2017, doi: 10.1109/SMC.2017.8122954.

M. Besserve, K. Jerbi, F. Laurent, S. Baillet, J. Martinerie, and L. Garnero, “Classification methods for ongoing EEG and MEG signals,” Biol Res, vol. 40, no. 4, pp. 415–437, 2007.

T. Shi, H. Wang, and C. Zhang, “Brain Computer Interface system based on indoor semi-autonomous navigation and motor imagery for Unmanned Aerial Vehicle control,” Expert Syst Appl, vol. 42, no. 9, pp. 4196–4206, 2015, doi: 10.1016/j.eswa.2015.01.031.

P. Kant, S. H. Laskar, J. Hazarika, and R. Mahamune, “CWT Based Transfer Learning for Motor Imagery Classification for Brain computer Interfaces,” J Neurosci Methods, vol. 345, p. 108886, Nov. 2020, doi: 10.1016/j.jneumeth.2020.108886.

W. Ouyang, K. Cashion, and V. K. Asari, “Electroencephelograph based brain machine interface for controlling a robotic arm,” in 2013 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1–7, 2013, doi: 10.1109/AIPR.2013.6749312.

M. H. Alomari, A. Samaha, and K. Alkamha, “Automated Classification of L/R Hand Movement EEG Signals using Advanced Feature Extraction and Machine Learning,” (IJACSA) International Journal of Advanced Computer Science and Applications, vol. 4, no. 6, 2013.

M. Plechawska-Wojcik, P. Wolszczak, R. Cechowicz, and K. Łygas, “Construction of neural nets in brain-computer interface for robot arm steering,” in 2016 9th International Conference on Human System Interactions (HSI), pp. 348–354, 2016, doi: 10.1109/HSI.2016.7529656.

W. Lu, Y. Wei, J. Yuan, Y. Deng, and A. Song, “Tractor Assistant Driving Control Method Based on EEG Combined With RNN-TL Deep Learning Algorithm,” IEEE Access, vol. 8, pp. 163269–163279, 2020, doi: 10.1109/ACCESS.2020.3021051.

P. Horki, T. Solis-Escalante, C. Neuper, and G. Müller-Putz, “Combined motor imagery and SSVEP based BCI control of a 2 DoF artificial upper limb,” Med Biol Eng Comput, vol. 49, no. 5, pp. 567–577, 2011, doi: 10.1007/s11517-011-0750-2.

M. J. Khan, K.-S. Hong, N. Naseer, and M. R. Bhutta, “Hybrid EEG-NIRS based BCI for quadcopter control,” in 2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), pp. 1177–1182, 2015, doi: 10.1109/SICE.2015.7285434.

J. Lee, K. Cha, H. Kim, J. Choi, C. Kim, and S. Lee, “Hybrid MI-SSSEP Paradigm for classifying left and right movement toward BCI for exoskeleton control,” in 2019 7th International Winter Conference on Brain-Computer Interface (BCI), pp. 1–3, 2019, doi: 10.1109/IWW-BCI.2019.8737319.

B. Chakravarthi, S. C. Ng, M. R. Ezilarasan, and M. F. Leung, “EEG-based emotion recognition using hybrid CNN and LSTM classification,” Front Comput Neurosci, vol. 16, Oct. 2022, doi: 10.3389/fncom.2022.1019776.

A. Bhardwaj, A. Gupta, P. Jain, A. Rani, and J. Yadav, “Classification of human emotions from EEG signals using SVM and LDA Classifiers,” in 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN), pp. 180–185, 2015, doi: 10.1109/SPIN.2015.7095376.

Z.-T. Liu, Q. Xie, M. Wu, W.-H. Cao, D.-Y. Li, and S.-H. Li, “Electroencephalogram Emotion Recognition Based on Empirical Mode Decomposition and Optimal Feature Selection,” IEEE Trans Cogn Dev Syst, vol. 11, no. 4, pp. 517–526, Dec. 2019, doi: 10.1109/TCDS.2018.2868121.

T. Song, W. Zheng, P. Song, and Z. Cui, “EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks,” IEEE Trans Affect Comput, vol. 11, no. 3, pp. 532–541, Jul. 2020, doi: 10.1109/TAFFC.2018.2817622.

S. K. Khare, V. Bajaj, and G. R. Sinha, “Adaptive Tunable Q Wavelet Transform-Based Emotion Identification,” IEEE Trans Instrum Meas, vol. 69, no. 12, pp. 9609–9617, Dec. 2020, doi: 10.1109/TIM.2020.3006611.

S. Issa, Q. Peng, and X. You, “Emotion Classification Using EEG Brain Signals and the Broad Learning System,” IEEE Trans Syst Man Cybern Syst, vol. 51, no. 12, pp. 7382–7391, Dec. 2021, doi: 10.1109/TSMC.2020.2969686.

M. K. Chowdary, J. Anitha, and D. J. Hemanth, “Emotion Recognition from EEG Signals Using Recurrent Neural Networks,” Electronics (Basel), vol. 11, no. 15, Jul. 2022, doi: 10.3390/electronics11152387.

J. Chen, X. Lin, W. Ma, Y. Wang, and W. Tang, “EEG-based emotion recognition for road accidents in a simulated driving environment,” Biomed Signal Process Control, vol. 87, no. 8, Jan. 2024, doi: 10.1016/j.bspc.2023.105411.

A. Elbrashy, A. Elgohr, A. Elshennawy, M. Rashad, and M. El‐fakharany, “Modeling of Heat Transfer and Airflow Inside Evacuated Tube Collector With Heat Storage Media: Experimental Validation Powered by Artificial Neural Network,” Heat Transfer, Nov. 2024, doi: 10.1002/htj.23215.

O. H. Hassan, M. S. Elhadidy, A. T. Elgohr, A. O. Ali, M. H. El-Mahdy, and M. Zaki, “Performance Evaluation of a Standalone Solar-Powered Irrigation System in Desert Regions Using PVsyst: A Comprehensive Analysis,” in 2024 25th International Middle East Power System Conference (MEPCON), pp. 1–6, 2024, doi: 10.1109/MEPCON63025.2024.10850121.