Volume 13, Issue 4

Epileptic Seizure Detection by Cascading Isolation Forest-Based Anomaly Screening and Easy Ensemble

Author

1Dr.Shajilin Loret J B, 2Mohamed Irshath, 3Praveen Sundar M, 4Sriram,

Abstract

Electroencephalogram (EEG) signal-based emotion recognition has attracted wide interests in recent years and has been broadly adopted in medical, affective computing, and other relevant fields. Depression has become a leading mental disorder worldwide. Evidence has shown that subjects with depression exhibit different spatial responses in neurophysiological signals from the healthy controls when they are exposed to positive and negative. We focus our analysis in the main aspects involved in the recognition process (e.g., subjects, features extracted, classifiers), and compare the works per them. We propose the emotional activation curve to demonstrate the activation process of emotions. The algorithm first extracts features from EEG signals and classifies emotions using machine learning techniques, in which different parts of a trial are used to train the proposed model and assess its impact on emotion recognition results. The primary objective of this project was to improve the performance of emotion recognition using brain signals by applying a novel and adaptive channel selection method that acknowledges that brain activity has a unique behavior that differs from one person to another and one emotional state to another. The result shows that our proposed method significantly improves the accuracy of classifying depression patients emotion as positive and negative

DOI

10.62226/ijarst20241344

PAGES : 1344- 1351 | 4 VIEWS | 2 DOWNLOADS

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