Emotion recognition using smart devices
By: Patil, Chandrakant
.
Contributor(s): Dhopeshwarkar, Mukta.
Publisher: Haryana IOSR - International Organization of Scientific Research 2022Edition: Vol.24(1), Jan-Feb.Description: 50-56p.Subject(s): Computer EngineeringOnline resources: Click here
In:
IOSR Journal of Computer Engineering (IOSR-JCE)Summary: The progress of communication systems has allowed us to think beyond traditional communication
systems, and the scene has been set for thought-oriented communication systems. Thousands of thoughts are
formed and then evaporate in a short period of time, yet certain notable concepts remain and we carry on with
our daily routines. EEG has advanced to the point that it is now possible to see the activity in the human brain
in a non-invasive manner. The approach for emotion identification utilizing EEG data recorded and processed
on smart devices is presented in this study. The results demonstrate the use of a computational neural network
to recognize emotions from EEG data. It was discovered that the correct categorization rate was 90.17 percent.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2022-2076 |
The progress of communication systems has allowed us to think beyond traditional communication
systems, and the scene has been set for thought-oriented communication systems. Thousands of thoughts are
formed and then evaporate in a short period of time, yet certain notable concepts remain and we carry on with
our daily routines. EEG has advanced to the point that it is now possible to see the activity in the human brain
in a non-invasive manner. The approach for emotion identification utilizing EEG data recorded and processed
on smart devices is presented in this study. The results demonstrate the use of a computational neural network
to recognize emotions from EEG data. It was discovered that the correct categorization rate was 90.17 percent.
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