Superior feature extraction, channel selection and classification methods are essential for designing electroencephalography (EEG) classification frameworks. However, the performance of most frameworks is limited by their improper channel selection methods and too specifical design, leading to high computational complexity, non-convergent procedure and narrow expansibility. In this paper, to remedy these drawbacks, we propose a fast, open EEG classification framework centralized by EEG feature compression, low-dimensional representation, and convergent iterative channel ranking. First, to reduce the complexity, we use data clustering to compress the EEG features channel-wise, packing the high-dimensional EEG signal, and endowing them with numerical signatures. Second, to provide easy access to alternative superior methods, we structurally represent each EEG trial in a feature vector with its corresponding numerical signature. Thus, the recorded signals of many trials shrink to a low-dimensional structural matrix compatible with most pattern recognition methods. Third, a series of effective iterative feature selection approaches with theoretical convergence is introduced to rank the EEG channels and remove redundant ones, further accelerating the EEG classification process and ensuring its stability. Finally, a classical linear discriminant analysis (LDA) model is employed to classify a single EEG trial with selected channels. Experimental results on two real world brain-computer interface (BCI) competition datasets demonstrate the promising performance of the proposed framework over state-of-the-art methods.

To improve the reliability and safety of myoelectric prosthetic control, many researchers tend to use multi-modal signals. The combination of electromyography (EMG) and forcemyography (FMG) has been proved to be a practical choice. However, an integrative and compact design of this hybrid sensor is lacking. This paper presents a novel modular EMG–FMG sensor; the sensing module has a novel design that consists of floating electrodes, which act as the sensing probe of both the EMG and FMG. This design improves the integration of the sensor. The whole system contains one data acquisition unit and eight identical sensor modules. Experiments were conducted to evaluate the performance of the sensor system. The results show that the EMG and FMG signals have good consistency under standard conditions; the FMG signal shows a better and more robust performance than the EMG. The average accuracy is 99.07% while using both the EMG and FMG signals for recognition of six hand gestures under standard conditions. Even with two layers of gauze isolated between the sensor and the skin, the average accuracy reaches 90.9% while using only the EMG signal; if we use both the EMG and FMG signals for classification, the average accuracy is 99.42%.

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Multichannel electroencephalography (EEG) is widely used in typical brain-computer interface (BCI) systems. In general, a number of parameters are essential for a EEG classification algorithm due to redundant features involved in EEG signals. However, the generalization of the EEG method is often adversely affected by the model complexity, considerably coherent with its number of undetermined parameters, further leading to heavy overfitting. To decrease the complexity and improve the generalization of EEG method, we present a novel l1-norm-based approach to combine the decision value obtained from each EEG channel directly. By extracting the information from different channels on independent frequency bands (FB) with l1-norm regularization, the method proposed fits the training data with much less parameters compared to common spatial pattern (CSP) methods in order to reduce overfitting. Moreover, an effective and efficient solution to minimize the optimization object is proposed. The experimental results on dataset IVa of BCI competition III and dataset I of BCI competition IV show that, the proposed method contributes to high classification accuracy and increases generalization performance for the classification of MI EEG. As the training set ratio decreases from 80 to 20%, the average classification accuracy on the two datasets changes from 85.86 and 86.13% to 84.81 and 76.59%, respectively. The classification performance and generalization of the proposed method contribute to the practical application of MI based BCI systems.

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Objective. Despite the encouraging pilot results of transcranial direct current stimulation (tDCS) revealing its effectiveness in neuromodulation, there are also studies reporting inconsistent outcomes. Apart from previously studied factors, such as the differences in head model structures, anodal displacements, electrode shape and size, and connector position, the hypothesis that the inevitable spatial mismatch between the electrolyte buffer and electrode might shape current flow in the cerebral cortex was tested in this work, and our results potentially explain some of the reported inconsistent outcomes. Approach. A finite element head model was built using cylinder electrodes with an arbitrary diameter of 2 cm. Current flow induced by different spatial mismatch types, degrees, and directions was simulated for three montages targeting the left motor cortex. Voxel-level current density differences and Jaccard index values of different percentiles for each mismatched configuration were calculated and compared throughout the cerebral cortex to determine the effect of electrode-electrolyte geometric mismatch. Main results. Spatial mismatch between the electrolyte buffer and electrode affected the current density distribution in the cerebral cortex to different extents, depending on the position of the return electrode and mismatch type, degree, and direction. Single cortical voxel current-density variance induced by the 50% excess or insufficient mismatch was as high as 14.44% or 38.04%, respectively. Moreover, the distribution of variance changed directionally with the mismatch orientation. Compared with the insufficient mismatch and single-directional mismatch, the excessive and symmetrical mismatch caused a less obvious effect on the current density distribution of tDCS. Specifically, the symmetrical excess electrolyte caused around 2%–4% current density changes for all the montages, with different degrees and directions of mismatch. When the target position was fixed at C3, maximum sensitivity to the electrolyte-electrode mismatch was achieved with Iz as the return electrode, compared with the other two choices. Further, more than 20% voxels with  >90% percentile of the peak current density values would shift position if  >30% insufficient geometric mismatch occurred for montage C3-Iz. Significance. Our findings suggest that special attention is required regarding the spatial matching of the electrolyte buffer to the electrode during tDCS to avoid unexpected large changes in current distribution.

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The design of a comfortable and functional prosthetic hand is still a challenge. This paper presents the design of a tendon-driven, 3D-printed, underactuated prosthetic hand. An improved structural design was developed to make the hand more flexible. Three fingers are equipped with abduction freedom at the metacarpophalangeal joints (MCP) to ensure natural enveloping for both cylinder and sphere-like objects. A force-sensing resistor (FSR) is adopted to measure the fingertip force of each finger. Experiments show that this type of structure design provides the hand with excellent dexterity, as the added abduction ensures natural enveloping grasp gestures for both cylinder and sphere-like objects. Moreover, a myoelectric control paradigm is implemented in the control system to demonstrate the feasibility.