Determination of angry condition based on EEG, speech and heartbeat
This paper determines the angry emotion condition by analyzing and recognizing speech signal, EEG signal, as well as detecting the heartbeat. For the speech analyzing experiment, several digital signal processing methods such as autocorrelation and linear predication technique was introduced to anal...
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| Main Authors: | , , , |
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| Format: | Article |
| Published: |
EBSCO Host Connection
2012
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| Subjects: | |
| Online Access: | http://connection.ebscohost.com/c/articles/86959192/determination-angry-condition-based-eeg-speech-heartbeat http://connection.ebscohost.com/c/articles/86959192/determination-angry-condition-based-eeg-speech-heartbeat http://eprints.uthm.edu.my/6128/1/Determination_of_Angry_Condition.pdf |
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| Summary: | This paper determines the angry emotion condition by analyzing and recognizing speech signal,
EEG signal, as well as detecting the heartbeat. For the speech analyzing experiment, several digital signal
processing methods such as autocorrelation and linear predication technique was introduced to analyze
the features. Then, Artificial Neural Network (ANN) was used to classify each parameter features such as
mean fundamental frequency, maximum fundamental frequency, standard deviation fundamental
frequency, mean amplitude, pause length ratio and first formant frequency to recognize the emotion. For
the EEG analysis, the raw EEG signal was undergone preprocessing to remove the artifacts to minimal.
Some features as mean, standard deviation, the peak amplitude, the peak amplitude in alpha band (PAA)
and the peak frequency in alpha band (PAF) of the EEG signals were extracted. The selected features
were classified by using ANN to obtain the maximum classification accuracy rate. Meanwhile, a heartbeat
monitoring circuit was developed to measure the heartbeat. The result showed that angry emotion has
relatively low condition in mean value, maximum peak amplitude and relatively high peak frequency in
alpha band (PAF) of the EEG signals. The mean fundamental frequency, standard deviation fundamental
frequency and mean intensity of the speech signal are good in determining the angry emotion. This
method can be used further to recognize angry emotion of patient during counseling session. |
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