Aiming at the implementation of brain-machine interfaces (BMI) for the aid of disabled people, this paper presents a system design for real-time communication between the BMI and programmable logic controllers (PLCs) to control an electrical actuator that could be used in devices to help the disabled. Motor imaginary signals extracted from the brain’s motor cortex using an electroencephalogram (EEG) were used as a control signal. The EEG signals were pre-processed by means of adaptive recursive band-pass filtrations (ARBF) and classified using simplified fuzzy adaptive resonance theory mapping (ARTMAP) in which the classified signals are then translated into control signals used for machine control via the PLC. A real-time test system was designed using MATLAB for signal processing, KEP-Ware V4 OLE for process control (OPC), a wireless local area network router, an Omron Sysmac CPM1 PLC and a 5 V/0.3A motor. This paper explains the signal processing techniques, the PLC's hardware configuration, OPC configuration and real-time data exchange between MATLAB and PLC using the MATLAB OPC toolbox. The test results indicate that the function of exchanging real-time data can be attained between the BMI and PLC through OPC server and proves that it is an effective and feasible method to be applied to devices such as wheelchairs or electronic equipment.
The Sixth International Brain-Computer Interface (BCI) Meeting was held 30 May-3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain-machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the general public. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and highlighting important issues and calls for action to support future research and development.
The integration of flexible sensors into human-machine interfaces (HMIs) is in increasing demand for intuitive and effective manipulation. Traditional glove-based HMIs, constrained by nonconformal rigid structures or the need for bulky batteries, face limitations in continuous operation. Addressing this, we introduce yarn-based bend sensors in our smart glove, which are wirelessly powered and harvest energy from a fully textile 5.8 GHz WiFi-band antenna receiver. These sensors exhibit a gauge factor (GF) of 5.60 for strains ranging from 0 to 10%. They show a consistent performance regardless of the straining frequency when being stretched and released at frequencies between 0.1 and 0.7 Hz. This reliability ensures that the sensor output is solely dependent on the yarn's elongation. Accurately detecting finger-bending movements from 0° to 90° in a virtual environment, the sensors enable enhanced degrees of freedom for human finger interaction. When integrated with advanced machine-learning techniques, the system achieves a classification accuracy of 98.75% for object recognition, demonstrating its potential for precise and accurate HMI. Unlike conventional near-field energy transfer methods that rely on magnetic flux and are limited by power loss over distance, our fully textile design effectively harvests microwave energy, showing no voltage deterioration up to 1 m away. This minimalist microwave-powered smart glove represents a significant advancement, offering a viable and practical solution for developing intuitive and reliable HMIs.
This paper presents a wheelchair navigation system based on a hidden Markov model (HMM), which we developed to assist those with restricted mobility. The semi-autonomous system is equipped with obstacle/collision avoidance sensors and it takes the electrooculography (EOG) signal traces from the user as commands to maneuver the wheelchair. The EOG traces originate from eyeball and eyelid movements and they are embedded in EEG signals collected from the scalp of the user at three different locations. Features extracted from the EOG traces are used to determine whether the eyes are open or closed, and whether the eyes are gazing to the right, center, or left. These features are utilized as inputs to a few support vector machine (SVM) classifiers, whose outputs are regarded as observations to an HMM. The HMM determines the state of the system and generates commands for navigating the wheelchair accordingly. The use of simple features and the implementation of a sliding window that captures important signatures in the EOG traces result in a fast execution time and high classification rates. The wheelchair is equipped with a proximity sensor and it can move forward and backward in three directions. The asynchronous system achieved an average classification rate of 98% when tested with online data while its average execution time was less than 1 s. It was also tested in a navigation experiment where all of the participants managed to complete the tasks successfully without collisions.
The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human-machine interface (HMI) technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG)-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2-11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy >90% of chosen combinations proved their ability to be used as command controllers.
A brain machine interface (BMI) design for controlling the navigation of a power wheelchair is proposed. Real-time experiments with four able bodied subjects are carried out using the BMI-controlled wheelchair. The BMI is based on only two electrodes and operated by motor imagery of four states. A recurrent neural classifier is proposed for the classification of the four mental states. The real-time experiment results of four subjects are reported and problems emerging from asynchronous control are discussed.
Hand-arm vibration syndrome (HAVS) is very common among the workers operating power tools and doing similar nature of work for long hours. Grass trimming is one of the operations that involves use of vibrating cutter, and results in hand-arm vibration among workers. In this study, the influence of several operating parameters (length of nylon cutting thread, engine speed and handle material) is investigated in terms of HAV. Data are analyzed via orthogonal array, main effect, signal-to-noise (S/N) ratio, and analysis of variance to determine the appropriate operating parameter levels to minimize HAV. Operating parameters under investigation are found to be influential in controlling HAV generation during grass trimming operation. Experiments are carried out for measuring hand-arm vibration using tri-axial accelerometer conforming the effectiveness of this approach. Results show that 100mm length of nylon thread, 3000+/-400rpm of engine speed and ABS handle material combination results in minimum HAV (HARM) of magnitude 2.76m/s(2). Through this study not only the optimal operating parameter levels for GTM are obtained, but also the main process parameters that affect the HAV are determined. The optimum HAV obtained through appropriate level selection of operating parameters, significantly reduces the occurrence of HAVS among the grass trimmers.