Patients in the neurofeedback and EMG-biofeedback groups showed hand improvement similar to conventional OT. Further studies are suggested to assign the best protocol for neurofeedback and EMG-biofeedback therapy.

No abstract

Abstract-The real-time adaptation between human and assistive devices can improve the quality of life for amputees, which, however, may be difficult to achieve since physical and mental states vary over time. This paper presents a co-adaptive humanmachine interface (HMI) that is developed to control virtual forearm prosthesis over a long period of operation. Direct physical performance measures for the requested tasks are calculated. Bioelectric signals are recorded using one pair of electrodes placed on the frontal face region of a user to extract the mental (affective) measures (the entropy of the alpha band of the forehead electroencephalography signals) while performing the tasks. By developing an effective algorithm, the proposed HMI can adapt itself to the mental states of a user, thus improving its usability. The quantitative results from 16 users (including an amputee) show that the proposed HMI achieved better physical performance measures in comparison with the traditional (nonadaptive) interface (p-value < 0:001). Furthermore, there is a high correlation (correlation coefficient < 0:9; p-value < :01) between the physical performance measures and self-report feedbacks based on the NASA TLX questionnaire. As a result, the proposed adaptive HMI outperformed a traditional HMI.

Many real world optimization problems are dynamic in which global optimum and local optimum change over time. Particle swarm optimization has performed well to find and track optimum in dynamic environments. In this paper, we propose a new particle swarm optimization algorithm for dynamic environments. The proposed algorithm utilizes FCM to adapt exclusion radios and utilize a local search on best swarm to accelerate progress of algorithm and adjust inertia weight adaptively. To improve the search performance, when the search areas of two swarms are overlapped, the worse swarms will be removed. Moreover, in order to track quickly the changes in the environment, all particles in the swarm convert to quantum particles when a change in the environment is detected. Experimental results on different dynamic environments modeled by moving peaks benchmark show that the proposed algorithm outperforms other PSO algorithms, for all evaluated environments.

This paper presents a novel human-machine interface for disabled people to interact with assistive systems for a better quality of life. It is based on multi-channel forehead bioelectric signals acquired by placing three pairs of electrodes (physical channels) on the Frontalis and Temporalis facial muscles. The acquired signals are passed through a parallel filter bank to explore three different sub-bands related to facial electromyogram, electrooculogram and electroencephalogram. The root mean square features of the bioelectric signals analyzed within non-overlapping 256 ms windows were extracted. The subtractive fuzzy c-means clustering method (SFCM) was applied to segment the feature space and generate initial fuzzy based Takagi-Sugeno rules. Then, an adaptive neuro-fuzzy inference system is exploited to tune up the premises and consequence parameters of the extracted SFCMs rules. The average classifier discriminating ratio for eight different facial gestures (smiling, frowning, pulling up left/right lips corner, eye movement to left/right/up/down) is between 93.04% and 96.99% according to different combinations and fusions of logical features. Experimental results show that the proposed interface has a high degree of accuracy and robustness for discrimination of 8 fundamental facial gestures. Some potential and further capabilities of our approach in human-machine interfaces are also discussed.