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We investigate the problem of achieving robust control of hand prostheses by the electromyogram (EMG) of transradial amputees in the presence of variable force levels, as these variations can have a substantial impact on the robustness of the control of the prostheses. We also propose a novel set of features that aim at reducing the impact of force level variations on the prosthesis controlled by amputees. These features characterize the EMG activity by means of the orientation between a set of spectral moments descriptors extracted from the EMG signal and a nonlinearly mapped version of it. At the same time, our feature extraction method processes the EMG signals directly from the time-domain to reduce computational cost. The performance of the proposed features is tested on EMG data collected from nine transradial amputees performing six classes of movements each with three force levels. Our results indicate that the proposed features can achieve significant reductions in classification error rates in comparison to other well-known feature extraction methods, achieving improvements of ≈ 6% to 8% in the average classification performance across all subjects and force levels, when training with all forces.

A method for the classification of finger movements for dexterous control of prosthetic hands is proposed. Previous research was mainly devoted to identify hand movements as these actions generate strong electromyography (EMG) signals recorded from the forearm. In contrast, in this paper, we assess the use of multichannel surface electromyography (sEMG) to classify individual and combined finger movements for dexterous prosthetic control. sEMG channels were recorded from ten intact-limbed and six below-elbow amputee persons. Offline processing was used to evaluate the classification performance. The results show that high classification accuracies can be achieved with a processing chain consisting of time domain-autoregression feature extraction, orthogonal fuzzy neighborhood discriminant analysis for feature reduction, and linear discriminant analysis for classification. We show that finger and thumb movements can be decoded accurately with high accuracy with latencies as short as 200 ms. Thumb abduction was decoded successfully with high accuracy for six amputee persons for the first time. We also found that subsets of six EMG channels provide accuracy values similar to those computed with the full set of EMG channels (98% accuracy over ten intact-limbed subjects for the classification of 15 classes of different finger movements and 90% accuracy over six amputee persons for the classification of 12 classes of individual finger movements). These accuracy values are higher than previous studies, whereas we typically employed half the number of EMG channels per identified movement.

Highlights► We recorded surface EMG signals with a biofeedback setup at 7 different contraction levels. ► We estimated the PDF, kurtosis and bicoherence index of the measured signals. ► We show that the EMG PDF at low contraction levels is super-Gaussian. ► At higher contraction forces, the EMG PDF tends to a Gaussian distribution.

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Partial reset is a simple and powerful tool for controlling the irregularity of spike trains fired by a leaky integrator neuron model with random inputs. In particular, a single neuron model with a realistic membrane time constant of 10 ms can reproduce the highly irregular firing of cortical neurons reported by Softky and Koch (1993). In this article, the mechanisms by which partial reset affects the firing pattern are investigated. Itisshown theoretically that partial reset is equivalent to the use of a time-dependent threshold, similar to a technique proposed by Wilbur and Rinzel (1983) to produce high irregularity. This equivalent model allows establishing that temporal integration and fluctuation detection can coexist and cooperate to cause highly irregular firing. This study also reveals that reverse correlation curves cannot be used reliably to assess the causes of firing. For instance, they do not reveal temporal integration when it takes place. Further, the peak near time zero does not always indicate coincidence detection. An alternative qualitative method is proposed here for that later purpose. Finally, it is noted that as the reset becomes weaker, the firing pattern shows a progressive transition from regular firing, to random, to temporally clustered, and eventually to bursting firing. Concurrently the slope of the transfer function increases. Thus, simulations suggest a correlation between high gain and highly irregular firing.

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