Classification of Emotions through EEG Signals using SVM and DNN
Veena N1, S Mahalakshmi2
1Veena N*, Asst. Professor, Department of Information Science & Engineering, BMSIT&M, Bengaluru, India.
2S Mahalakshmi, Asst. Professor, Department of Information Science & Engineering, BMSIT&M, Bengaluru, India.
Manuscript received on January 13, 2020. | Revised Manuscript received on January 21, 2020. | Manuscript published on February 10, 2020. | PP: 206-209 | Volume-9 Issue-4, February 2020. | Retrieval Number: I8103078919/2020©BEIESP | DOI: 10.35940/ijitee.I8103.029420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Emotions are important for Humans both at work place and in their life. Emotions helps us to communicate with others, to take decisions, in understand others etc., Emotions recognition not only helps us to solve the mental illness but also are important in various application such as Brain Computer Interface , medical care and entertainment This paper mainly deals with how Emotions are Classified through EEG Signals using SVM (Support Vector machine) and DNN (Deep Neural Networks) . Applying the most appropriate algorithm to detect the emotional state of a person and play the corresponding song in the playlist. Brain signals can be collected using EEG (electroencephalography) devices.
Keywords: Emotions. SVM, DNN, EEG, Emotions
Scope of the Article: Classification