Emotion Recognition Using Time-Frequency Distribution and GLCM Features from EEG Signals
Deep learning techniques are commonly used for emotion recognition from Electroencephalography (EEG) signals. However, some disadvantages of employing these classifiers are the high memory requirements and the low number of available EEG samples in datasets. This work proposes a novel approach for increasing the number of extracted features based on…
Emotion Recognition Using Time-Frequency Distribution and GLCM Features from EEG Signals Deep learning techniques are commonly used for emotion recognition from Electroencephalography (EEG) signals. However, some disadvantages of employing these classifiers are the high memory requirements and the low number of available EEG samples in datasets. This work proposes a novel approach for increasing the number of extracted features based on… image-processing
Emotion Recognition Using TimeFrequency Distribution GLCM Features EEG Signals Deep learning technique commonly used emotion recognition Electroencephalography EEG signal However disadvantage employing classifier high memory requirement low number available EEG sample datasets work proposes novel approach increasing number extracted feature based on… imageprocessing