Emotion Analysis for Personality Inference from EEG Signals
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Product Description
Aim:
To
design a MATLAB Model for Individual’s Emotion detection through computed EEG using
DEAP dataset and making the people to relax by playing music spontaneously
Abstract:
Emotion
plays an important role in our daily life and work. Real-time assessment and
regulation of emotion will improve people’s life and make it better. For
example, in the communication of human-machine-interaction, emotion recognition
will make the process more easy and natural. Another example, in the treatment
of patients, especially those with expression problems, the real emotion state
of patients will help doctors to provide more appropriate medical care. In
recent years, emotion recognition from EEG has gained mass attention. Also it
is a very important factor in brain computer interface (BCI) systems, which
will effectively improve the communication between human and machines. Various
features and extraction methods have been proposed for emotion recognition from
EEG signals, including time domain techniques, frequency domain techniques,
joint time-frequency analysis techniques, and other strategies. Here we proposed
a reliable method which efficient in detection of individual’s health This
design can detect the condition of the individual person using image classifier
and EEG analyzer using adaptive threshold detection method.
Proposed System:
In
this system, emotions are predicted by EEG signal acquired from brain waves.
This waves are grouped in the form of alpha, beta, delta, and so on. Later this
are splitted and processed to find feelings of individuality. Then if suppose
person is in depressed state are relaxed by playing music and in joy state person are maintained in that state
for prolonged time.
Advantages of Proposed
System:
Three
dimensional condition Detection
Emotion
recognition will help us to get accurate info.
Emotion in depressed state are recovered by relaxing mode.
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