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In recent years, there has been a growing demand for awareness of depression among
emotions and depression is considered to affect physical bodies [3,4]. Therefore, we need
a system to recognize the depression.
Although there are other techniques such as image processing, we consider
utilization of electroencephalographs (EEGs) due to its advantage of low cost. As a
advantage of emotion recognition by EEG, it can be used even if it is not in front of the
device with a camera such as a personal computer, and not only emotion recognition but
also other recognition such as concentration degree and stress can be done, and the
goodness of the affinity of the headset and the electroencephalogram, It is possible to use
the call center where the change of feelings is considered a problem, and to study. Girardi
showed that emotional recognition in EEG is effective in various biological signals [5].
It is also an effective tool for detecting dementia, alcoholism, autism spectrum, etc. [6]
[7] [8]. There are various kinds of emotion recognition methods such as image
recognition of face [9] [10], speech [11], heart rate [12], EEGs, but in this study we
decided to treat emotion recognition by EEG.
EEG is more expensive than the number of electrodes, and there are problems such
as restricting the motion. Moreover, there is a problem that the more electrodes take
longer to install. There are various types of EEG, such as expensive ones with a large
number of electrodes, inexpensive ones with low number of electrodes, and fewer
electrodes, but the arrangement of electrodes can be changed. Individual differences may
appear in EEG due to the shape of the skull, sebum on the scalp, and the presence of
hair.[13]
The characteristic of EEG is that the information that can be obtained by the electrode
site of the electroencephalogram is different. Therefore, the purpose of this study was to
investigate the combination of the drop and the high contribution electrodes in the
emotion recognition in EEG. What type of electroencephalogram should be used to
investigate the depression using a small number of electrodes, if the
electroencephalogram that can change the electrode arrangement, we thought that it
might be an index that it was easy to recognize the drop by the arrangement of the
electrode.
In this paper, we explain a method to reduce electrodes in emotional recognition of
depression by using an EEG from the EEG data of DEAP. As a result, it was confirmed
that the multiple correlation coefficient of EEG data and depression increased as the
number of electrodes increased. The relationship between EEG data acquired from the
electrode site selected by the method in this research and the feeling of depression was
clarified.
The rest of the paper is organized as follows. Section 2 includes a brief description
of the MWM system architecture and how to make feature quantities and investigation
by LARS. Section 3 describes the evaluation result and research. Section 4 presents the
most important conclusions and provides directions for additional research.
2. Implementation of MWM system
In this chapter, we will describe how to proceed with the analysis in this research. Figure
1 shows an overview of how to proceed with analysis.
Y.Tsurugasaki etal. / IdentificationofEffectiveEEGElectrodes forDepressionSensing 153
Intelligent Environments 2019
Workshop Proceedings of the 15th International Conference on Intelligent Environments
- Title
- Intelligent Environments 2019
- Subtitle
- Workshop Proceedings of the 15th International Conference on Intelligent Environments
- Authors
- Andrés Muñoz
- Sofia Ouhbi
- Wolfgang Minker
- Loubna Echabbi
- Miguel Navarro-Cía
- Publisher
- IOS Press BV
- Date
- 2019
- Language
- German
- License
- CC BY-NC 4.0
- ISBN
- 978-1-61499-983-6
- Size
- 16.0 x 24.0 cm
- Pages
- 416
- Category
- Tagungsbände