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Spectral band power features are one of the most widely used features in the studies of electroencephalogram (EEG)-based emotion recognition. The power spectral density of EEG signals is partitioned into different bands such as delta, theta, alpha and beta band etc. Though based on neuroscientific findings, the partition of frequency bands is somewhat on an ad-hoc basis, and the definition of frequency...
Emotion recognition is an integral part of affective computing. An affective brain-computer-interface (BCI) can benefit the user in a number of applications. In most existing studies, EEG (electroencephalograph)-based emotion recognition is explored in a classificatory manner. In this manner, human emotions are discretized by a set of emotion labels. However, human emotions are more of a continuous...
An integration of real-time EEG-based human emotion recognition algorithms in brain-computer interfaces can make the user's experience more complete, more engaging, less emotionally stressful or more stressful depending on the target of the application. Valence component of emotion, level of pleasantness, is one of the most important criteria of online assessment of social processes from brain signals...
Mental workload can be recognized from Electroencephalogram (EEG) and can be used to assess mental efforts of the user performing different tasks. In this work, we designed and implemented an experiment for mental workload recognition related to no-task, visual task, auditory task and multitask performance. The Simultaneous Capacity SIMKAP test was used to induce different levels of mental workload...
Everyone experiences stress in life. Moderate stress can be beneficial to human, however, excessive stress is harmful to the health. To monitor stress, different methods can be used. In this work, an algorithm for stress level recognition from Electroencephalogram (EEG) is proposed. To validate the algorithm, an experiment is designed and carried out with 9 subjects. A Stroop colour-word test is used...
Stability of algorithms is very important for electroencephalogram (EEG) based applications. Stable features should exhibit consistency among repeated measurements of the same subject. Previously, power features were reported to be one of the most stable EEG features in medical application. In this paper, stability of features in emotion recognition algorithms is studied. Our hypothesis is that the...
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