[LR] Swarm Intelligence for EEG Channel Selection in Emotion Recognition

[This review is intended solely for my personal learning] Paper Info DOI: 10.1007/978-3-031-05409-9_23 Title: A Swarm Intelligence Approach: Combination of Different EEG-Channel Optimization Techniques to Enhance Emotion Recognition Authors: Sabahudin Balic, Lukas Kleybolte, and Christian Märtin Prior Knowledge EEG-based Emotion Recognition: EEG captures electrical brain activity via multiple electrodes. Its high temporal resolution makes it suitable for decoding emotional states in real-time. However, the full 32-channel setup is often computationally expensive and redundant. Swarm Intelligence: Algorithms like Particle Swarm Optimization (PSO), Cuckoo Search (CS), and Grey Wolf Optimizer (GWO) mimic social behavior of animals to solve optimization problems. Feature vs. Channel Selection: Traditional feature selection targets discriminative features across frequency bands, while channel selection focuses on spatially optimizing electrode positions. Goal To evaluate and compare different EEG channel selection techniques—both classical and swarm intelligence-based—to optimize emotion classification performance while significantly reducing computation time. ...

March 22, 2025 · 3 min

[LR] quEEGNet: QAI for Biosignal Processing

[This review is intended solely for my personal learning] Paper Info arXiv: 2210.00864v1 Title: quEEGNet: Quantum AI for Biosignal Processing Authors: Toshiaki Koike-Akino, Ye Wang Prior Knowledge Biosignal Processing in Human-Machine Interfaces (HMI) Biosignals such as EEG (electroencephalogram), EMG (electromyogram), and ECoG (electrocorticogram) are fundamental to developing brain-computer interfaces (BCI) and HMIs. However, biosignals are subject to high variability and noise, requiring robust machine-learning models for feature extraction and classification. ...

February 11, 2025 · 3 min