Abstract
Affective human-robot interaction requires lightweight software and cheap wearable devices that could further this field. However, the estimation of emotions in real-time poses a problem that has not yet been optimized. An optimization is proposed for the emotion estimation methodology including artifact removal, feature extraction, feature smoothing, and brain pattern classification. The challenge of filtering artifacts and extracting features, while reducing processing time and maintaining high accuracy results, is attempted in this work. First, two different approaches for real-time electro-oculographic artifact removal techniques are tested and compared in terms of loss of information and processing time. Second, an emotion estimation methodology is proposed based on a set of stable and meaningful features, a carefully chosen set of electrodes, and the smoothing of the feature space. The methodology has proved to perform on real-time constraints while maintaining high accuracy on emotion estimation on the SEED database, both under subject dependent and subject independent paradigms, to test the methodology on a discrete emotional model with three affective states.
Highlights
The use of Electroencephalography (EEG) signals for emotion estimation has been in the point of view of the field for the last decades
Under the paradigm of emotion recognition, robots will allow the development of automatic systems for the treatment and evaluation of the brain patterns of patients, taking into account the emotional content and, to have the ability to adapt their behavior as the mood of the patient changes dynamically
It can be noted that independent component analysis (ICA)-W focuses on the artifactual data better than EAWICA, the latter seems to affect the signal in all the frequency ranges
Summary
The use of Electroencephalography (EEG) signals for emotion estimation has been in the point of view of the field for the last decades. From the perspective of the field of robotics, emotions estimation can be performed by evaluating the dynamical changes over facial expressions, body language, voice tone, EEG patterns, Real-Time EEG Emotion Estimation and physiological signals, related to the equilibrium between the parasympathetic and sympathetic autonomous systems. The EEG is a non-invasive method of high temporal resolution that could allow real-time recognition of emotional responses. It can provide a better understanding of the user’s behavior and emotional responses which involve facial expression, tone of voice, or body gestures, which may remain hidden as is the case for patients with expression and mobility problems. In this article, EEG patterns will be analyzed and related to emotional responses, as they may provide a different perspective on patients’ emotional responses
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