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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmu Komputer (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana JUTIK : Jurnal Teknologi Informasi dan Komputer Jurnal RESISTOR (Rekayasa Sistem Komputer) Indonesian Journal of Electrical Engineering and Computer Science Indonesian Journal of Data and Science Jurnal Pengabdian Informatika (JUPITA) Jurnal Nasional Teknologi Informasi dan Aplikasinya
Gst. Ayu Vida Mastrika Giri
Department Of Computer Science, FMIPA, Universitas Udayana
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Journal : IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

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Electroencephalogram-Based Emotion Classification Using Machine Learning and Deep Learning Techniques Mastrika Giri, Gst Ayu Vida; Radhitya, Made Leo
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 3 (2024): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.96665

Abstract

 Electroencephalogram (EEG) records brain activity as electrical currents to discern emotions. As interest in human-computer emotional connections rises, reliable and implementable emotion recognition algorithms are essential. This study classifies EEG waves using machine and deep learning. A four-channel Muse EEG headband recorded neutral, negative, and positive emotions for the publicly available Feeling Emotions EEG dataset. Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) were utilized for deep learning, while SVM, K-NN, and MLP were used for machine learning. The models were assessed for accuracy, precision, recall, and F1-Score. SVM, K-NN, and MLP have accuracy scores of 0.98, 0.95, and 0.97. Deep learning methods CNN, LSTM, and GRU had 0.98, 0.82, and 0.97 accuracy. SVM and CNN surpassed other approaches in accuracy, precision, recall, and F1-Score. The research shows that machine learning and deep learning can classify EEG signals to identify emotions. High accuracy results, especially from SVM and CNN, suggest these models could be used in emotion-aware human-computer interaction systems. This study adds to EEG-based emotion classification research by revealing model selection and parameter tweaking strategies for better categorization.
Co-Authors Agus Harjoko Agus Muliantara Al Habib Muhammad Anak Agung Istri Ngurah Eka Karyawati Andika Putra, Ida Bagus Angriani, Husni Arianata Putra, I Gusti Bagus Sutha Dhita, I Made Ryan Prana Edo Krishnanda Aditya Gede Agung Aji Andar Sakti Giri, I Nyoman Yusha Tresnatama Gusto Gibeon Ginting I Gede Arta Wibawa I Gede Arta Wibawa I Gede Diva Dwijayana I Gede Laksmana Yudha I Gede Santi Astawa I Gusti Ngurah Anom Cahyadi Putra I Ketut Gede Suhartana I Ketut Gede Suhartana I Komang Ari Mogi I Komang Ari Mogi I Made Ryan Prana Dhita I Made Widiartha I Made Yoga Mahendra I Putu Ananta Wijaya I WAYAN SANTIYASA I Wayan Supriana izmy alwiah musdar Jaya, I Nyoman Wiratma Kadek Nanda Banyu Permana Komang Arsa Wiguna Leo Radhitya, Made Luh Arida Ayu Rahning Putri Luh Ristiario Muhammad Arief Budiman Ngurah Agus Sanjaya ER Ni Ketut Intan Setiawati Ni Made Elvina Aryadhika Putri Ni Putu Novia Ardiyanti Ni Putu Suci Paramita Nyoman Hendradinata Dharma Pasha Renaisan Prasetya, Putu Rikky Mahendra Pratama Putra, I Putu Rizky Putu Audy Cipta Pratiwi Radhitya, Made Leo Raharja, Made Agung Siaka, Made Bayu Maha Krisna Sutanti, Putu Asri Sri Taruk, Medi Wiratama Putra, I Putu Andi Yoga, I Putu Harta