Accurate EEG-based emotion recognition using LSTM and BiLSTM networks

Yaacob, Mashkuri and Gunawan, Teddy Surya and Abu Bakar, Muhammad Izwan Fazry and Yusoff, Siti Hajar and Kartiwi, Mira and Md Yusoff, Nelidya (2024) Accurate EEG-based emotion recognition using LSTM and BiLSTM networks. In: IEEE 10th International Conference on Smart Instrumentation, Measurement and Applications, 30-31 July 2024, Bandung, Indonesia.

PDF - Published Version Restricted to Registered users only Download (1MB) | Request a copy

Abstract

Emotion recognition is crucial for advancing human-computer interaction and mental health diagnostics. Unlike speech or image-based methods, EEG provides a direct, non-manipulable measure of neural activity, offering a more reliable insight into genuine emotional states. This study addresses the challenge of accurately recognizing emotions from EEG signals by leveraging deep learning techniques. The primary objective is to develop an efficient emotion recognition system using Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) networks. EEG data were preprocessed, and features were extracted using wavelet packet decomposition. The LSTM and BiLSTM models were then trained to classify emotions into binary and multiclass categories. Results demonstrated that the BiLSTM model achieved superior accuracy, with 91.78% for binary classification and 85.21% for multiclass classification. These findings highlight the potential of advanced deep learning models in enhancing emotion recognition accuracy, contributing significantly to fields such as affective computing and mental health monitoring.

Item Type:	Proceeding Paper (Slide Presentation)
Uncontrolled Keywords:	EEG-based Emotion Recognition, Long ShortTerm Memory (LSTM), Bidirectional LSTM (BiLSTM), Deep Learning, Affective Computing.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1001 Production of electric energy. Powerplants
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering Kulliyyah of Engineering
Depositing User:	dr siti hajar yusoff
Date Deposited:	22 Oct 2024 14:26
Last Modified:	22 Oct 2024 14:26
URI:	http://irep.iium.edu.my/id/eprint/115181

Actions (login required)

View Item