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An effective optimized deep learning for emotion classification from EEG signals

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Abstract

Electroencephalography (EEG) signals can be used for emotion recognition (ER), which is an effective method for determining someone's mental state. However, because an EEG signal is non-stationary, the ER is a fascinating challenge. Additionally, the categorization of the mood patterns in the EEG signal makes use of signal processing techniques to extract pertinent data from EEG signals. As a result, the fractional chimp optimization algorithm (FrChOA), which was developed for this work, is introduced as an improved deep learning technique for choosing the best channel and classifying emotions from EEG signals. By merging the chimp optimization algorithm (CA) with fractional calculus, the created FrChOA is modeled (ChOA). Pre-processing, optimal channel selection, feature extraction, and human emotion categorization are the processing stages carried out in this instance by the ER. First, the low pass filtering technique is used to pre-process the incoming EEG signal. The best channel is then chosen using a developed algorithm called FrChOA, which bases its choice on classification accuracy. In order to increase classification performance, the essential features are extracted at the end of the feature extraction procedure. Additionally, the deep neuro-fuzzy network, whose training process is created FrChOA, is used for emotion classification. The developed algorithm also produced the best results, as evidenced by its testing accuracy, sensitivity, and specificity of 0.8848, 0.8763, and 0.8946, respectively.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, SV University College of Engineering, Sri Venkatewara University, Tirupathi, 517502, Andhrapradesh, India

    Sittiahgari Lokesh

  2. Department of Electronics and Communication Engineering, Sri Venkatewara University, Tirupathi, 517502, Andhrapradesh, India

    Tadipathri Sreenivasulu Reddy

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  1. Sittiahgari Lokesh
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  2. Tadipathri Sreenivasulu Reddy
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Lokesh, S., Reddy, T.S. An effective optimized deep learning for emotion classification from EEG signals. SIViP 17, 1631–1642 (2023). https://doi.org/10.1007/s11760-022-02373-2

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