Biology subjects

Choudhari, V.

Publications and source records attributed to Choudhari, V..

3 recordsLinked to original sources

bioRxiv2025

Speaker Identity is Robustly Encoded in Spatial Patterns of Intracranial EEG for Attention Decoding

The human auditory cortex robustly tracks attended speech, yet it remains unclear if speaker identity is encoded in spatial patterns of neural activity independent of temporal dynamics. Here, we demonstrate that the identity of an attended speaker is reliably reflected in distinct, time-invariant spatial activation maps in human intracranial EEG (iEEG). Leveraging these "neural fingerprints", we developed a novel framework for Auditory Attention Decoding (AAD) that shifts from traditional temporal envelope tracking to spatial speaker identification. By decoupling the decoding of "who" is speaking from "when" they are speaking, our modular system achieves state-of-the-art speech extraction, particularly in short time windows (<2 seconds) where temporal models typically fail. Furthermore, we observed a reciprocal shift in neural activity during attentional switches, confirming that these spatial codes dynamically track listener intent. These findings establish that speaker identity is a robust, spatially distributed feature in the auditory cortex, offering a high-speed, complementary mechanism for neuro-steered hearing technologies.

neuroscience↗

bioRxiv2025

Large Language Models Reveal the Neural Tracking of Linguistic Context in Attended and Unattended Multi-Talker Speech

Large language models (LLMs) capture long-range contextual structure in natural language and have recently been shown to align with the human brains contextualized linguistic encoding. This makes them a promising computational probe for studying how context-dependent linguistic information is represented during natural speech perception. Speech perception often occurs in multi-talker environments, where attention must dynamically select among competing streams, yet how contextual information from attended and unattended speech is neurally encoded remains underexplored. Here, we investigate how auditory attention modulates neural tracking of context-dependent linguistic representations using electrocorticography (ECoG) and stereoelectroencephalography (sEEG) recordings from three epilepsy patients engaged in a two-conversation "cocktail party" paradigm. To model neural responses to attended and unattended speech streams, we used contextual word embeddings generated by large language models. We find that LLM-derived features reliably predict brain activity for the attended stream and that contextual information from the unattended stream also contributes to neural prediction. Importantly, these contributions extend beyond low-level acoustic features and shallow syntactic information, and depend on the surrounding linguistic context. Moreover, neural tracking of the unattended stream reflects shorter-range contextual integration than that of the attended stream. Together, these findings indicate that neural responses to speech reflect context-dependent linguistic representations from multiple concurrent speech streams, with attention modulating the depth and timescale of contextual integration. Our results highlight the utility of LLMs for probing higher-level linguistic representations in complex, naturalistic listening environments.

neuroscience↗

bioRxiv2024

Brain-controlled augmented hearing for spatially moving conversations in multi-talker environments

Focusing on a specific conversation amidst multiple interfering talkers presents a significant challenge, especially for the hearing-impaired. Brain-controlled assistive hearing devices aim to alleviate this problem by separating complex auditory scenes into distinct speech streams and enhancing the attended speech based on the listeners neural signals using auditory attention decoding (AAD). Departing from conventional AAD studies that relied on oversimplified scenarios with stationary talkers, we present a realistic AAD task that mirrors the dynamic nature of acoustic settings. This task involves focusing on one of two concurrent conversations, with multiple talkers taking turns and moving continuously in space with background noise. Invasive electroencephalography (iEEG) data were collected from three neurosurgical patients as they focused on one of the two moving conversations. We propose an enhanced brain-controlled assistive hearing system that combines AAD and a binaural speaker-independent speech separation model. The separation model unmixes talkers while preserving their spatial location and provides talker trajectories to the neural decoder to improve auditory attention decoding accuracy. Our subjective and objective evaluations show that the proposed system enhances speech intelligibility and facilitates conversation tracking while maintaining spatial cues and voice quality in challenging acoustic environments. This research demonstrates the potential of our approach in real-world scenarios and marks a significant step towards developing assistive hearing technologies that adapt to the intricate dynamics of everyday auditory experiences. TAKEAWAYS- Brain-controlled hearing device for scenarios with moving conversations in multi-talker settings, closely mimicking real-world listening environments - Developed a binaural speech separation model that separates speech of moving talkers while retaining their spatial locations, enhancing auditory perception and auditory attention decoding - Proposed system enhances speech intelligibility and reduces listening effort in realistic acoustic scenes

neuroscience↗