SOM-Based Class Discovery for Emotion Detection Based on DEAP Dataset

2018 ◽  
Vol 10 (1) ◽  
pp. 15-26
Author(s):  
Aladdin Ayesh ◽  
Miguel Arevalillo-Herra´ez ◽  
Pablo Arnau-González
Keyword(s):  
Ground Truth ◽  
Emotion Detection ◽  
Neighbor Distance ◽  
Eeg Signals ◽  
Self Organizing Maps ◽  
The Public ◽  
Class Discovery ◽  
Eeg Data ◽  
Public Dataset ◽  
Self Organizing

This paper investigates the possibility of identifying classes by clustering. This study includes employing Self-Organizing Maps (SOM) in identifying clusters from EEG signals that could then be mapped to emotional classes. Beginning by training varying sizes of SOM with the EEG data provided from the public dataset: DEAP. The produced graphs showing Neighbor Distance, Sample Hits, and Weight Position are examined. Following that, the ground-truth label provided in DEAP is tested, in order to identify correlations between the label and the clusters produced by the SOM. The results show that there is a potential of class discovery using SOM-based clustering. It is then concluded that by evaluating the implications of this work and the difficulties in evaluating its outcome.

scholarly journals Supervised and Semi-Supervised Self-Organizing Maps for Regression and Classification Focusing on Hyperspectral Data

2019 ◽  
Vol 12 (1) ◽  
pp. 7 ◽  
Author(s):  
Felix M. Riese ◽  
Sina Keller ◽  
Stefan Hinz
Keyword(s):  
Land Cover ◽  
Reference Data ◽  
Ground Truth ◽  
Hyperspectral Data ◽  
Physical Parameters ◽  
Learning Approaches ◽  
Self Organizing Maps ◽  
Python Package ◽  
Self Organizing

Machine learning approaches are valuable methods in hyperspectral remote sensing, especially for the classification of land cover or for the regression of physical parameters. While the recording of hyperspectral data has become affordable with innovative technologies, the acquisition of reference data (ground truth) has remained expensive and time-consuming. There is a need for methodological approaches that can handle datasets with significantly more hyperspectral input data than reference data. We introduce the Supervised Self-organizing Maps (SuSi) framework, which can perform unsupervised, supervised and semi-supervised classification as well as regression on high-dimensional data. The methodology of the SuSi framework is presented and compared to other frameworks. Its different parts are evaluated on two hyperspectral datasets. The results of the evaluations can be summarized in four major findings: (1) The supervised and semi-Supervised Self-organizing Maps (SOM) outperform random forest in the regression of soil moisture. (2) In the classification of land cover, the supervised and semi-supervised SOM reveal great potential. (3) The unsupervised SOM is a valuable tool to understand the data. (4) The SuSi framework is versatile, flexible, and easy to use. The SuSi framework is provided as an open-source Python package on GitHub.

scholarly journals DEEPHER: Human Emotion Recognition Using an EEG-Based DEEP Learning Network Model

2021 ◽  
Vol 10 (1) ◽  
pp. 32
Author(s):  
Akhilesh Kumar ◽  
Awadhesh Kumar
Keyword(s):  
Deep Learning ◽  
Computer Games ◽  
Intelligent System ◽  
Sensor Data ◽  
Computational Time ◽  
Identification System ◽  
Emotion Detection ◽  
Eeg Signals ◽  
Emotion Identification ◽  
Eeg Data

Emotion identification and categorization have been emerging in the brain machine interface in the current era. Audio, visual, and electroencephalography (EEG) data have all been shown to be useful for automated emotion identification in a number of studies. EEG-based emotion detection is a critical component of psychiatric health assessment for individuals. If EEG sensor data are collected from multiple experimental sessions or participants, the underlying signals are invariably non-stationary. As EEG signals are noisy, non-stationary, and non-linear, creating an intelligent system that can identify emotions with good accuracy is challenging. Many researchers have shown evidence that EEG brain waves may be used to determine feelings. This study introduces a novel automated emotion identification system that employs deep learning principles to recognize emotions through EEG signals from computer games. EEG data were obtained from 28 distinct participants using 14-channel Emotive Epoc+ portable and wearable EEG equipment. Participants played four distinct emotional computer games for five minutes each, with a total of 20 min of EEG data available for each participant. The suggested framework is simple enough to categorize four classes of emotions during game play. The results demonstrate that the suggested model-based emotion detection framework is a viable method for recognizing emotions from EEG data. The network achieves 99.99% accuracyalong with less computational time.

scholarly journals Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection

2020 ◽  
Vol 10 (21) ◽  
pp. 7677
Author(s):  
Gen Li ◽  
Jason J. Jung
Keyword(s):  
Feature Selection ◽  
Eeg Signal ◽  
Emotion Detection ◽  
Eeg Signals ◽  
Training Time ◽  
Eeg Data ◽  
Signal Preprocessing ◽  
The Difference ◽  
Electroencephalogram Eeg ◽  
Signal Similarity

Emotion detection is an important research issue in electroencephalogram (EEG). Signal preprocessing and feature selection are parts of feature engineering, which determines the performance of emotion detection and reduces the training time of the deep learning models. To select the efficient features for emotion detection, we propose a maximum marginal approach on EEG signal preprocessing. The approach selects the least similar segments between two EEG signals as features that can represent the difference between EEG signals caused by emotions. The method defines a signal similarity described as the distance between two EEG signals to find the features. The frequency domain of EEG is calculated by using a wavelet transform that exploits a wavelet to calculate EEG components in a different frequency. We have conducted experiments by using the selected feature from real EEG data recorded from 10 college students. The experimental results show that the proposed approach performs better than other feature selection methods by 17.9% on average in terms of accuracy. The maximum marginal approach-based models achieve better performance than the models without feature selection by 21% on average in terms of accuracy.

A Psychographic Segmentation of Kuwaiti Travelers Using Self-Organizing Maps

2019 ◽  
Vol 24 (1) ◽  
pp. 87-92 ◽  
Author(s):  
Yvette Reisinger ◽  
Mohamed M. Mostafa ◽  
John P. Hayes
Keyword(s):  
Self Organizing Maps ◽  
Self Organizing
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