Evaluation of Classifiers for Emotion Detection While Performing Physical and Visual Tasks: Tower of Hanoi and IAPS
| Autor: | Syed Muhammad Zeeshan Iqbal, Shahnawaz Qureshi, Hamad Javaid, Craig Lindley, Johan Hagelbäck |
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| Rok vydání: | 2018 |
| Předmět: |
medicine.diagnostic_test
business.industry Computer science media_common.quotation_subject Emotion detection Decision tree Empathy Electroencephalography Machine learning computer.software_genre 01 natural sciences Support vector machine 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Eeg data Perception medicine Robot 030212 general & internal medicine Artificial intelligence 0101 mathematics business computer media_common |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783030010539 IntelliSys (1) |
| Popis: | With the advancement in robot technology, smart human-robot interaction is of increasing importance for allowing the more excellent use of robots integrated into human environments and activities. If a robot can identify emotions and intentions of a human interacting with it, interactions with humans can potentially become more natural and effective. However, mechanisms of perception and empathy used by humans to achieve this understanding may not be suitable or adequate for use within robots. Electroencephalography (EEG) can be used for recording signals revealing emotions and motivations from a human brain. This study aimed to evaluate different machine learning techniques to classify EEG data associated with specific affective/emotional states. For experimental purposes, we used visual (IAPS) and physical (Tower of Hanoi) tasks to record human emotional states in the form of EEG data. The obtained EEG data processed, formatted and evaluated using various machine learning techniques to find out which method can most accurately classify EEG data according to associated affective/emotional states. The experiment confirms the choice of a method for improving the accuracy of results. According to the results, Support Vector Machine was the first, and Regression Tree was the second best method for classifying EEG data associated with specific affective/emotional states with accuracies up to 70.00% and 60.00%, respectively. In both tasks, SVM was better in performance than RT. |
| Databáze: | OpenAIRE |
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