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    Transfer Learning for Classifying Motor Imagery EEG: A Comparative Study
    (2021-01-01)
    Limpiti, Tulaya
    ;
    Seetanathum, Kornthum
    ;
    Sricom, Natchaya
    ;
    Puttarak, Nattakan
    Motor imagery (MI) is the cognitive process when a person imagines performing a specific movement of their body. The corresponding electroencephalographic (EEG) signals can be measured externally to the head using scalp electrodes. Such signals have been applied in healthcare brain-computer-interface (BCI) systems, for example, motor rehabilitation and prosthetics control. These systems convert different MI EEG input signals to directives, so their performances depend on the efficiency of the embedded signal classification algorithm. In this paper we investigate the effectiveness of transfer learning in classifying the MI EEG data. The Continuous Wavelet Transform (CWT) is used to construct the scalograms, which serve as the inputs to the deep learning structure. The efficacies of five pre-trained networksAlexNet, ResNet18, ResNet50, InceptionV3 and ShuffleNet-are evaluated on the BCI competition IV data set 2a. Binary (left hand vs. right hand) and four-class (left hand, right hand, both feet, and tongue) classifications are trained and tested using fivefold cross validation. The result indicates that using the CWT with transfer learning models provides very high classification accuracies. The ResNet18 network achieves the best accuracies in both cases at 95.03±2.95% and 91.86±2.90%, respectively. In addition, we examine the effect of different time-frequency features on the classification performance by comparing the scalogram of the CWT and the spectogram of the Short-Time Fourier Transform (STFT) as the inputs. It is found that the CWT is the preferred choice as it is superior to the STFT.
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    A simple laboratory test of music therapy for insufficient sleep
    (2016-02-04)
    Phasukkit, Pattarapong
    ;
    Mahrozeh, Nurhayatee
    ;
    Kumngern, Montree
    ;
    Tungjitkusolmun, Supan
    This article studies the behavior of human sleep found the insufficient sleep problem due to the brain performance. This experiment uses devices such as sleeptracker to save the results of a sleep and electroencephalogram (EEG) to detect brainwaves while enjoying music. However, this study considers only a general sleep and sleep event that a relaxed with the music. The findings indicated the music can help to reduce the problems of insufficient sleep. As a result, volunteers are developing the sleep as sufficiently and efficiently due to get the best quality of sleep in the life. It is also available to deploy to the general person in order to solve the problem of insufficient sleep.