Biology subjects

An, W. W.

Publications and source records attributed to An, W. W..

3 recordsLinked to original sources

bioRxiv2026

AVOCODO: An open-source multimodal annotation platform for developmental EEG

Behavioral annotation of synchronized video recordings is an essential step in developmental electroencephalography (EEG) research, supporting both the identification of behavior-related artifacts and the investigation of brain-behavior relationships. Existing annotation workflows, however, are often fragmented: proprietary EEG software provides limited flexibility for behavioral coding, whereas dedicated behavioral annotation platforms typically lack native integration with EEG data. We developed AVOCODO (Audio/VideO CODing Optimization), an open-source MATLAB-based software platform that integrates synchronized behavioral annotation directly into the EEG workflow. AVOCODO reads native EGI MFF recordings, synchronizes embedded video with EEG, visualizes the audio spectrogram to facilitate precise annotation of vocalizations, and writes user-defined behavioral events directly back into the original MFF recording as native EEG event markers while simultaneously exporting annotations as CSV files. The software supports fully customizable behavioral coding schemes, optional EEG visualization for quality control, and reloading of previously annotated recordings for review and inter-rater verification. Since its initial development in 2024, AVOCODO has been applied internally across five developmental EEG studies involving approximately 500 pediatric participants and more than 3,000 EEG recordings. By bridging behavioral annotation and EEG preprocessing within a unified open-source workflow, AVOCODO has the potential to improve the efficiency, reproducibility, and scalability of behavioral annotation in developmental EEG research.

bioinformatics↗

bioRxiv2026

Representational similarity analysis of EEG reveals multiple spatiotemporal dynamics of selective attention

Selective attention allows listeners to follow a target speaker in complex scenes, but less is known about the neural mechanisms that control this capacity. We collected electroencephalography (EEG) during an auditory attention task encompassing spatial attention, talker-based attention, and passive listening. Time-resolved representational similarity analysis (RSA) of broadband scalp voltage and alpha-band oscillations revealed distinct representational trajectories in the two measurements: scalp voltage produced brief peaks in task discriminability shortly after the target cue and around 300 ms after target onset, whereas alpha-band power emerged more slowly and remained elevated throughout the trial. These patterns distinguish attentive from passive conditions and spatial from talker attention. Furthermore, broadband activity and alpha power encode complementary information, while other frequency bands contribute little independent explanatory power. These results demonstrate that RSA allows a unified interpretation of evoked and induced EEG measures, revealing how distinct neural computations jointly encode the spatiotemporal dynamics of selective attention.

neuroscience↗

bioRxiv2023

Neural representation of spatial and non-spatial auditory attention in EEG signals

Neural representation, capturing the content and format of encoded information, provides insight into the internal states of neural units. Studies of neural representation contrast with studies of neural processes, which focus on how one neural unit influences another. Representational similarity analysis (RSA), a multivariate analysis approach, has been used in previous studies to explore the neural representation of object categories in various neuroimaging modalities. In this study, we employed RSA to examine the neural representation of executive function. We designed an experiment involving a rich set of conditions where participants engaged in an auditory task requiring either spatial or non-spatial attention. We extracted representational features from their electroencephalography (EEG) scalp voltage and alpha power and compared these features with ideal conceptual models representing perfect categorization of different attentional states. The results demonstrate the feasibility of investigating internal cognitive states using RSA. Specifically, we identified time intervals during which attentional state contrasts, such as differences between attention types or locations, manifested in the measured neural responses from scalp voltage and alpha power distributions.

neuroscience↗