Published in Journal: MDPI Sensors, 2022
This paper proposes two CNN models (M1 and M2) for emotion recognition from brain signals using EEG data, achieving nearly perfect accuracy.
Recommended citation: Akter, S., Prodhan, R. A., Pias, T. S., Eisenberg, D., & Fresneda Fernandez, J. (2022). M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity. *Sensors*, 22(21), 8467.
Published in 27th IEEE International Conference on Computer and Information Technology (ICCIT 2024), 2024
This paper introduces a custom SegUNet architecture with a VGG19 backbone for improved segmentation of pediatric dental panoramic radiographs.
Recommended citation: Ovi, Md Ohiduzzaman, Maliha Sanjana, Fahad Fahad, Mahjabin Runa, Zarin Tasnim Rothy, Tanmoy Sarkar Pias, A. M. Islam, and Rumman Ahmed Prodhan. "Enhanced Pediatric Dental Segmentation Using a Custom SegUNet with VGG19 Backbone on Panoramic Radiographs." arXiv preprint arXiv:2503.06321 (2025).
Published in Conference: 4th International Conference on Activity and Behavior Computing, 2024
This study empirically analyzes the contribution of different brain regions to emotion recognition using an optimal EEG electrode set.
Recommended citation: Prodhan, R. A., Akter, S., Adnan, M. A., & Pias, T. S. (2024). Optimal EEG Electrode Set for Emotion Recognition from Brain Signals: An Empirical Quest. In *4th International Conference on Activity and Behavior Computing* (pp. 67-88), October 27-29, 2022, London, United Kingdom. CRC Press.
Published in Conference: International Conference on Machine Intelligence and Emerging Technologies, 2023
This paper presents a multitask learning approach for emotion recognition from brain waves leveraging residual connections.
Recommended citation: Prodhan, R. A., Akter, S., Mujib, M. B., Adnan, M. A., & Pias, T. S. (2023, June). Emotion recognition from brain wave using multitask machine learning leveraging residual connections. In *International Conference on Machine Intelligence and Emerging Technologies* (pp. 121-136). Cham: Springer Nature Switzerland.
Published in Conference: Sentiment Analysis and Deep Learning, ICSADL 2022, 2023
This paper evaluates the effectiveness of various machine learning algorithms and deep learning models for classifying sentiment from EEG signals.
Recommended citation: Akter, S., Prodhan, R. A., Mujib, M. B., Adnan, M. A., & Pias, T. S. (2023). Evaluating the Effectiveness of Classification Algorithms for EEG Sentiment Analysis. In *Sentiment Analysis and Deep Learning: Proceedings of ICSADL 2022* (pp. 195-212). Singapore: Springer Nature Singapore.
Published in Undergraduate Thesis, University of Asia Pacific, 2022
This thesis explores emotion recognition using facial expressions and EEG signals for improved accuracy.