Real-time EEG-based emotion monitoring using stable features

Abstract

In human-computer interaction (HCI), electroencephalogram (EEG) signals can be added as an additional input to computer. An integration of real-time EEG-based human emotion recognition algorithms in human-computer interfaces can make the users experience more complete, more engaging, less emotionally stressful or more stressful depending on the target of the applications. Currently, the most accurate EEG-based emotion recognition algorithms are subject-dependent, and a training session is needed for the user each time right before running the application. In this paper, we propose a novel real-time subject-dependent algorithm with the most stable features that gives a better accuracy than other available algorithms when it is crucial to have only one training session for the user and no re-training is allowed subsequently. The proposed algorithm is tested on an affective EEG database that contains five subjects. For each subject, four emotions (pleasant, happy, frightened and angry) are induced, and the affective EEG is recorded for two sessions per day in eight consecutive days. Testing results show that the novel algorithm can be used in real-time emotion recognition applications without re-training with the adequate accuracy. The proposed algorithm is integrated with real-time applications "Emotional Avatar" and "Twin Girls" to monitor the users emotions in real time

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EEG-based mind driven type writer by fuzzy radial basis function neural classifier

“…Besides these, parietal and motor cortex regions are found to take significantly active participation during the experiment. Additionally, literature [1][2][3][4][5][6] reveals that temporal lobe is highly associated with human speech signal processing. Therefore, we select P3, P4 and Pz (from parietal lobe), T1, T2, T3, T4, T5 and T6 (from temporal lobe) and C3, C4 and Cz (from motor cortex region) for extracting necessary information by applying signal processing techniques.…”

“…There are so many research work based on real time EEG-based human emotion recognition [2], [3] and EEG based stress monitoring is described in paper [4]. In paper [5], one channel of ICA data is taken as input which increases classification accuracy to 87%.…”

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“…Besides, existing signals are not enough for high accuracy feature extraction. Several approaches introduce more features in different analysis domains to capture extra information about the state of the brain [107,117,200,203,213,216,224]. Consequently, feature extraction is one of the major challenges in designing BCI systems; it is determined based on the features and on the appropriate transformation.…”

“…These proposed systems aim to explore or improve EEG-based emotion recognition systems. [2,39,41,42,49,50,57,61,63,92,104,108,109,117,131,136,149,152,157,173,174,185,186,189,191,[195][196][197][198][199][200][201][202][203][204][205][206][207][208][209]217,219,[223][224][225]229,[262][263][264][265][266]<...>…”