An Open Non-Invasive EEG Dataset for Spontaneous Auditory Attention Switch Decoding

Auditory brain-computer interfaces (BCIs) based on non-invasive electrodes offer significant potential for advancing the understanding of selective auditory attention mechanisms and enabling natural human-computer interaction. Despite growing interest in auditory attention research, publicly available datasets focused on spontaneous auditory attention switching remain limited, particularly those with high-quality electroencephalography (EEG) recordings in realistic listening environments. To address this gap, we present the Auditory Attention Switching Dataset (AASD), a non-invasive EEG dataset designed to investigate spontaneous selective auditory attention switching during naturalistic auditory processing. The dataset captures both sustained attention and spontaneous attention switching events through EEG signals. A baseline decoding model is introduced to verify data integrity and demonstrate its potential for practical applications. This open-access resource lays the foundation for developing algorithms for spontaneous auditory attention switching and advancing research in natural-scenario auditory BCIs.