This paper proposes a robotic hand rehabilitation device for grasp training. The device is designed for stroke patients to train and recover their hand grasp function in order to undertake activities of daily living (ADL). The device consists of a control unit, two small actuators, an infrared (IR) sensor, and pressure sensors in the grasp handle. The advantages of this device are that it is small in size, inexpensive, and available for use at home without specialist's supervision. In addition, a novel patient-driven strategy based on the patient's movement intention detected by the pressure sensors without bio-signals is introduced. Once the system detects a patient's movement intention, it triggers the robotic device to move the patient's hand to form the normal grasping behavior. This strategy may encourage stroke patients to participate in rehabilitation training to recover their hand grasp function and it may also enhance neural plasticity. A user study was conducted in order to investigate the usability, acceptability, satisfaction, and suggestions for improvement of the proposed device. The results of this survey included positive reviews from therapists and a stroke patient. In particular, therapists expected that the proposed patient-driven mode can motivate patients for their rehabilitation training and it can be effective to prevent a compensational strategy in active movements. It is expected that the proposed device will assist stroke patients in restoring their grasp function efficiently.
The brain and muscles both of which are composed of top-down structure occur the connectivity with the change of Electroencephalogram(EEG) and Electromyogram(EMG). In this paper, we studied the difference of functional connectivity between brain and muscles that by applying coherence method to EEG and EMG signals when users exercised upper limb with and without the movement intention. The changes in the EEG and EMG signals were inspected using coherence method. During the upper limb exercise, the mu (8~14 Hz) and beta (15~30 Hz) rhythms of the EEG signal at the motor cortex area are activated. And then the beta and piper (30~60 Hz) rhythms of the EMG signal are activated as well. The result of coherence analysis between EEG and EMG showed the coefficient of active exercise including movement intention is significantly higher than passive exercise. The coherence relations between cognitive response and muscle movement could interpret that the connectivity between the brain and muscle appear during active exercise with movement intention. The feature of coherence between brain and muscles by the status of movement intention will be useful in designing the rehabilitation system requiring feedback depending on the users' movement intention status.
It is well established that motor action/imagery provokes an event-related desynchronization (ERD) response at specific brain areas with specific frequency ranges, typically the sensory motor rhythm and beta bands. However, there are individual differences in both brain areas and frequency ranges which can be used to identify ERD. This often results in low classification accuracy of ERD, which makes it difficult to implement of BCI application such as the control of external devices and motor rehabilitation. To overcome this problem, an individually optimized solution may be desirable for enhancing the accuracy of detecting motor action/imagery with ERD rather than a global solution for all BCI users. This paper presents a method based on a genetic algorithm to find individually optimized brain areas and frequency ranges for ERD classification. To optimize these two components, we designed a chromosome consisting of 64-bit elements represented by a binary number and another 9-bit elements using 512 pre-defined frequency ranges (2^9). The average value of the significant level is set for the properties of the objective function for use in a t-test, (p < 0.01) depending on the random selection from a concurrent population. As a result, contralateral ERD responses in the spatial domain with individually optimized frequency ranges showed a significant difference between resting and motor action. The ERD responses for motor imagery, on the other hand, led to a bilateral pattern with a narrow frequency band compared to motor action. This study provides the possibility of selecting optimized electrode positions and frequency bands which can lead to high levels of ERD classification accuracy.
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