Single Channel

Single-channel analysis focuses on extracting meaningful information from a single data stream, contrasting with multi-channel approaches. Current research emphasizes developing robust algorithms, often employing convolutional neural networks (CNNs) and transformers, to overcome challenges posed by noise and limited data in single-channel scenarios, particularly within EEG and audio processing. This research is significant because it enables cost-effective and accessible applications in diverse fields, including sleep stage classification, seizure detection, and speech enhancement, where multi-channel data acquisition may be impractical or expensive. Improved single-channel methods offer the potential for wider deployment of diagnostic and monitoring tools.