A novel method for hands rehabilitation using optimal control of fractional order singular system and biological signals

•A novel rehabilitation approach combines EEG and EMG signal classification to control robotic hand movements.•Machine learning methods and optimized CNN-LSTM-SVM framework are used to improve classification accuracy.•Application of fractional order singular to model and control rehabilitation processes, addressing movement constraints.•Analytical-numerical solution to fractional order singular system and overcoming convergence issues in numerical methods.•Integrating dynamic and algebraic constraints reduces control costs and enhances system usability for home rehabilitation. In recent years, significant advances have been made in biological signal processing, allowing for the control of robotic devices. This paper introduces an innovative hand rehabilitation method for improving brain-hand connectivity using a robotic hand based on cognitive robotics. The process begins by recording the user’s electroencephalogram (EEG) and electromyogram (EMG) signals while performing hand movements in two different positions. Next, a method for effective EEG and EMG channel selection is developed, followed by two algorithms for classification of various hand movement patterns. The first algorithm incorporates preprocessing, window selection, feature extraction, and machine learning algorithms. The second algorithm uses automatic feature extraction via optimized CNN-LSTM-SVM. The rehabilitation process is controlled using fractional order singular optimal control based on the identified hand movement patterns and optimal controller design. This control approach is involved in both time-invariant and also time-varying systems. A mathematical model of the constrained rehabilitation process using a robotic hand is derived using fractional order singular theory. The problem of fractional order singular optimal control is solved via a numerical-analytical approach that utilizes Hamiltonian and orthogonal polynomials. A master supervises the entire process, and adjustments are made to each component if the error exceeds a desired threshold. Finally, a simulation is conducted to demonstrate the effectiveness of the proposed method. Conclusions regarding the feasibility and potential advantages of utilizing cognitive robotics-based control for robotic hand rehabilitation are shown.