TY - GEN
T1 - EGNN-C+
T2 - 2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems, EAIS 2024
AU - Leite, Daniel
AU - Silva, Alisson
AU - Casalino, Gabriella
AU - Sharma, Arnab
AU - Fortunato, Danielle
AU - Ngomo, Axel Cyrille
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - We introduce a modified incremental learning algorithm for evolving Granular Neural Network Classifiers (eGNN-C+). We use double-boundary hyper-boxes to represent granules, and customize the adaptation procedures to enhance the robustness of outer boxes for data coverage and noise suppression, while ensuring that inner boxes remain flexible to capture drifts. The classifier evolves from scratch, incorporates new classes on the fly, and performs local incremental feature weighting. As an application, we focus on the classification of emotion-related patterns within electroencephalogram (EEG) signals. Emotion recognition is crucial for enhancing the realism and interactivity of computer systems. The challenge lies exactly in developing high-performance algorithms capable of effectively managing individual differences and non-stationarities in physiological data without relying on subject-specific information. We extract features from the Fourier spectrum of EEG signals obtained from 28 individuals engaged in playing computer games-A public dataset. Each game elicits a different predominant emotion: boredom, calmness, horror, or joy. We analyze individual electrodes, time window lengths, and frequency bands to assess the accuracy and interpretability of resulting user-independent neural models. The findings indicate that both brain hemispheres assist classification, especially electrodes on the temporal (T8) and parietal (P7) areas, alongside contributions from frontal and occipital electrodes. While patterns may manifest in any band, the Alpha (8-13Hz), Delta (1-4Hz), and Theta (4-8Hz) bands, in this order, exhibited higher correspondence with the emotion classes. The eGNN-C+ demonstrates effectiveness in learning EEG data. It achieves an accuracy of 81.7% and a 0.002933 interpretability using 10-second time windows, even in face of a highly-stochastic time-varying 4-class classification problem.
AB - We introduce a modified incremental learning algorithm for evolving Granular Neural Network Classifiers (eGNN-C+). We use double-boundary hyper-boxes to represent granules, and customize the adaptation procedures to enhance the robustness of outer boxes for data coverage and noise suppression, while ensuring that inner boxes remain flexible to capture drifts. The classifier evolves from scratch, incorporates new classes on the fly, and performs local incremental feature weighting. As an application, we focus on the classification of emotion-related patterns within electroencephalogram (EEG) signals. Emotion recognition is crucial for enhancing the realism and interactivity of computer systems. The challenge lies exactly in developing high-performance algorithms capable of effectively managing individual differences and non-stationarities in physiological data without relying on subject-specific information. We extract features from the Fourier spectrum of EEG signals obtained from 28 individuals engaged in playing computer games-A public dataset. Each game elicits a different predominant emotion: boredom, calmness, horror, or joy. We analyze individual electrodes, time window lengths, and frequency bands to assess the accuracy and interpretability of resulting user-independent neural models. The findings indicate that both brain hemispheres assist classification, especially electrodes on the temporal (T8) and parietal (P7) areas, alongside contributions from frontal and occipital electrodes. While patterns may manifest in any band, the Alpha (8-13Hz), Delta (1-4Hz), and Theta (4-8Hz) bands, in this order, exhibited higher correspondence with the emotion classes. The eGNN-C+ demonstrates effectiveness in learning EEG data. It achieves an accuracy of 81.7% and a 0.002933 interpretability using 10-second time windows, even in face of a highly-stochastic time-varying 4-class classification problem.
UR - http://www.scopus.com/inward/record.url?scp=85199276064&partnerID=8YFLogxK
U2 - 10.1109/EAIS58494.2024.10570033
DO - 10.1109/EAIS58494.2024.10570033
M3 - Conference contribution
AN - SCOPUS:85199276064
T3 - IEEE Conference on Evolving and Adaptive Intelligent Systems
BT - IEEE International Conference on Evolving and Adaptive Intelligent Systems 2024, EAIS 2024 - Proceedings
A2 - Iglesias Martinez, Jose Antonio
A2 - Baruah, Rashmi Dutta
A2 - Kangin, Dimitry
A2 - De Campos Souza, Paulo Vitor
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 May 2024 through 24 May 2024
ER -