Emotion Recognition using EEG Signal Analysis | Machine Learning

Author and Presenter: Muhammad Bilal Sajid
Summary:
In this study, we introduce a machine-learning model that recognizes emotions from Electroencephalogram (EEG) signals. Using the publicly available DREAMERs dataset, we identify 4 emotions - sadness, joy, anger, and pleasure. Our approach involves segmenting EEG signals into 30-second segments, noise reduction with a band-stop filter, and extraction of 5 frequency bands. After applying filters, we obtained 6 time-based features. To address class imbalance, we employ ADASYN, and feature selection is done using the Chi-square algorithm. The Fine K-nearest neighbor (KNN) classifier achieves a 91.1% accuracy, taking around 25 seconds for emotion detection via EEG signals.
#emotions
#eeg
#emotionrecognition
#machinelearning
#machinelearningprojects
#machinelearningproject
#intellcity

[@sumairaziz9219]

Great work.

[@sinemkaya7132]

İs there an article for your work ?

[@reel_2.0.0]

😍

[@mattnaganidhi942]

Interesting 🤔

[@deepeshbhattarai2]

Hello Where can you provide me your code zip file?

[@ElectroDeCODE]

Thanks for your reply. See the comment below.

[@waqasahmad55202]

❤🎉

[@saikrishna1444]

Can you provide the source code?

[@gamingRealm47]

love from Mars

[@ayushikotwal6293]

can u plz give ur source code

[@ElectroDeCODE]

Please contact the admin via email or facebook.

[@zainmalik11223]

❤🎉