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IKKN Predictor: An EEG Signal Based Emotion Recognition for HCI

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Abstract

Emotion recognition is the process of identifying the human emotion through their facial expression. However, it is a challenging task to determine the emotions of mentally challenged people. Therefore the emotion classification and prediction is the main aim of the research developed in the past years with different techniques. The number of state-of-the-art literature is reviewed using these techniques for the prediction of emotions. This paper carried out three stages of the analysis such as pre-processing, feature extraction and selection then emotion recognition using IKNN. The performance of this algorithm is evaluated using five parameters in SEED platform of Matlab simulation tool. This method of classification gives better performance regarding accuracy, precision, recall and mean square error. Therefore based on the analysis, this paper summarises the deep study of different classification strategies with its performance.

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Authors and Affiliations

  1. Computer Science and Engineering Department, Shri Guru Gobind Singhji Institute of Engineering and Technology, Vishnupuri, Nanded, Maharashtra, 431 606, India

    Sujata Bhimrao Wankhade

  2. Department of Electronics and Telecommunication Engineering, Shri Guru Gobind Singhji Institute of Engineering and Technology, Vishnupuri, Nanded, Maharashtra, 431 606, India

    Dharmapal Dronacharya Doye

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  1. Sujata Bhimrao Wankhade
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  2. Dharmapal Dronacharya Doye
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Correspondence to Sujata Bhimrao Wankhade.

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Wankhade, S.B., Doye, D.D. IKKN Predictor: An EEG Signal Based Emotion Recognition for HCI. Wireless Pers Commun 107, 1135–1153 (2019). https://doi.org/10.1007/s11277-019-06328-8

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