JRM Vol.32 No.4 pp. 724-730
doi: 10.20965/jrm.2020.p0724


Human-Wants Detection Based on Electroencephalogram Analysis During Exposure to Music

Shin-ichi Ito, Momoyo Ito, and Minoru Fukumi

Tokushima University
2-1 Minami-josanjima, Tokushima 770-8506, Japan

February 20, 2020
May 18, 2020
August 20, 2020
wants detection, electroencephalogram, listening to music, convolutional neural networks, support vector machine
Human-Wants Detection Based on Electroencephalogram Analysis During Exposure to Music

Human-wants detection method

We propose a method to detect human wants by using an electroencephalogram (EEG) test and specifying brain activity sensing positions. EEG signals can be analyzed by using various techniques. Recently, convolutional neural networks (CNNs) have been employed to analyze EEG signals, and these analyses have produced excellent results. Therefore, this paper employs CNN to extract EEG features. Also, support vector machines (SVMs) have shown good results for EEG pattern classification. This paper employs SVMs to classify the human cognition into “wants,” “not wants,” and “other feelings.” In EEG measurements, the electrical activity of the brain is recorded using electrodes placed on the scalp. The sensing positions are related to the frontal cortex and/or temporal cortex activities although the mechanism to create wants is not clear. To specify the sensing positions and detect human wants, we conducted experiments using real EEG data. We confirmed that the mean and standard deviation values of the detection accuracy rate were 99.4% and 0.58%, respectively, when the target sensing positions were related to the frontal and temporal cortex activities. These results prove that both the frontal and temporal cortex activities are relevant for creating wants in the human brain, and that CNN and SVM are effective for the detection of human wants.

Cite this article as:
Shin-ichi Ito, Momoyo Ito, and Minoru Fukumi, “Human-Wants Detection Based on Electroencephalogram Analysis During Exposure to Music,” J. Robot. Mechatron., Vol.32, No.4, pp. 724-730, 2020.
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Last updated on Feb. 25, 2021