Adaptive learning interface used physiological signals

The purpose of the article is the development of an interface which closely adapts to the individual. By quantifying the frustration as the human manipulates machines from a biomedical signal and making it into teaching signals of machine learning (ML), we aim at the development of a system in which the machine adapts to the human. The authors extract the characteristic vector of whether the examinee is in discomfort or not from electroencephalograms (EEG) and electromyograms (EMG). An artificial neural network (ANN) is employed to extract the vector. For the machine learning, reinforcement learning is used and the rewards are an extracted signal from physiological signals. As a basic experiment for extracting discomfort and comfort from the physiological signals, the EEG measurement experiment was carried out under an unpleasant sound environment for 20 examinees. The input signals to ANN for the characteristic vector extraction was examined. By affixing piezoelectric films on the eyebrow, the movement of the eyebrow was measured. Finally, the results of measuring EEG and EMG simultaneously under the situation in which frustration accumulated for the examinee are shown. We use reinforcement learning (RL) to control the behavior of the Khepera robot