Recognition of motor intentions from EEGs of the same upper limb by signal traceability and Riemannian geometry features.

Introduction: The electroencephalographic (EEG) based on the motor imagery task is derived from the physiological electrical signal caused by the autonomous activity of the brain. Its weak potential difference changes make it easy to be overwhelmed by noise, and the EEG acquisition method has a natural limitation of low spatial resolution. These have brought significant obstacles to high-precision recognition, especially the recognition of the motion intention of the same upper limb. Methods: This research proposes a method that combines signal traceability and Riemannian geometric features to identify six motor intentions of the same upper limb, including grasping/holding of the palm, flexion/extension of the elbow, and abduction/adduction of the shoulder. First, the EEG data of electrodes irrelevant to the task were screened out by low-resolution brain electromagnetic tomography. Subsequently, tangential spatial features are extracted by the Riemannian geometry framework in the covariance matrix estimated from the reconstructed EEG signals. The learned Riemannian geometric features are used for pattern recognition by a support vector machine with a linear kernel function. Results: The average accuracy of the six classifications on the data set of 15 participants is 22.47%, the accuracy is 19.34% without signal traceability, the accuracy is 18.07% when the features are the filter bank common spatial pattern (FBCSP), and the accuracy is 16.7% without signal traceability and characterized by FBCSP. Discussion: The results show that the proposed method can significantly improve the accuracy of intent recognition. In addressing the issue of temporal variability in EEG data for active Brain-Machine Interfaces, our method achieved an average standard deviation of 2.98 through model transfer on different days' data.

Study of Mass Transfer Enhancement of Electrolyte Flow Field by Rotating Cathode in Through-Mask Electrochemical Micromachining.

To solve the problem of the nonuniform distribution of temperature and electrolytic products in the electrolyte flow field during through-mask electrochemical micromachining, the use of a rotating cathode with surface structures is proposed. The rotation of the cathode increases the efficiency of heat and mass transfer by the electrolyte flow. Simulations are performed to analyze the influence of the type of surface structure, the number of surface structures, and the rotational speed of the cathode on the electrolyte flow field. The results show that the use of a rotating cathode with surface structures significantly improves the mass transfer efficiency of the electrolyte flow field in comparison with a conventional cathode structure, and, in particular, a grooved rotating cathode can increase the outlet flow velocity by about 23%. An experimental demonstration of micropit array processing shows that the use of a grooved rotating cathode increases the mass transfer efficiency by 34% and the processing efficiency by nearly 40% compared with a smooth-surfaced rotating cathode. The grooved rotating cathode also gives the highest machining accuracy. Using this cathode, a uniform micropit array with an average micropit diameter of 201.83 µm, a diameter standard deviation of 3.49 µm, and a depth standard deviation of 0.87 µm is processed.