This paper focuses on the developments of asynchronous motor imagery (MI) based brain-computer interfaces (BCIs) applications, signal processing and machine learning to provide some basic capabilities for consumer grade products. For the proposed MI detection technique, two channels of FC5 and FC6 according to 10-20 system over primary motor area are used to recognize 3 mental tasks of tongue, left hand and right hand movements. The amplitude features of EEG signals are extracted from power spectral analysis especially in mu rhythm (8 -12 Hz) and low beta wave (12 -16 Hz) bands. MI features were obtained from offline analysis, and then applied to neural network (NN) with particle swarm optimization (PSO). The classification paradigm then applied to real-time BCI for humanoid robot control applications in terms of recognized MI classes from subjects. According to the experiments of 45 trials for a healthy subject, the NN-based MI recognition accuracy with PSO is 91%.
This paper proposes a reconfigurable biopotential amplifier for a medical instrumentation course in the department of electrical engineering. With the reconfigurable biopotential amplifier, the students are able to measure their biopotential signals such as electromyography, electrocardiography and electrooculography in the class. In addition, reconfigurations of the circuit board can be achieved by forming different combinations of the circuit modules to investigate the effects of employing the amplifiers and filters, as well as to practice the topics of various biopotential signal sources in terms of adjusting the configurations of the circuit board. At the same time, variable resistors are further used to adjust the amplifier gains and frequency conditions. Moreover, a microcontroller is provided for converting the analog signals to digital forms so that the biopotential signals can be displayed and recorded with a computer program. Therefore, the reconfigurable biopotential amplifier is a low cost and compact educational tool. The students can practice this module only with a personal computer which connects a USB node to provide the DC power and serial data communication. In addition to the improvement on the learning achievements for the biopotential signal measurement experiments within the lecture topics in the medical instrumentation course, the reconfigurable biopotential amplifier also benefits the hands-on skills of students for designing the amplifiers and filters in medical instrumentations.
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