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Wenbo Ma

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YNIMG Journal 2025 Journal Article

Quantifying task-locked information transmission between cortical areas with TMS-EEG

Zhaohuan Ding
Wenbo Ma
Leixiao Feng
Mingsha Zhang
Xiaoli Li

OBJECTIVE: This study aims to develop TMS-EEG (Transcranial magnetic stimulation combined with EEG) technology to detect task-locked neural network activation and dynamically quantify information transmission. APPROACH: 30 participants performed visually guided gap saccade tasks while TMS-EEG data were recorded, with the TMS pulses delivered to prefrontal cortex (PFC) and posterior parietal cortex (PPC) at different task stages. The directed transfer function (DTF) method was applied to TMS-EEG data to indicate the information flow. By analyzing the channel combinations associated with the PFC and PPC, we calculated differences in information flow within the alpha, beta, and gamma frequency bands to determine whether TMS-EEG could quantitatively characterize the direction of information flow between cortical areas. MAIN RESULTS: Analysis of eye tracker data revealed that all participants successfully performed the saccade task, with a correct rate exceeding 90 %. The mean saccade latency was 132.25 ± 22.59 ms after target appearance. Stimulation of the PFC and PPC revealed significant differences in information flow in the gamma bands at different time points. Specifically, during the preparatory period, the C3 electrode acts as a hub for incoming information from O1, later transitioning to send information towards F4 and O1 post-target. Then, P3 emerges as a hub, sending data towards P4, with connectivity between them intensifying post 100 ms from the target's appearance. SIGNIFICANCE: This study utilized DTF values derived from TMS-EEG to characterize information flow between cortical areas during the gap saccade task. This approach provides a novel method for quantifying dynamic changes in connectivity and causality between cortical areas during task processing.

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AAAI Conference 2021 Conference Paper

Ref-NMS: Breaking Proposal Bottlenecks in Two-Stage Referring Expression Grounding

Long Chen
Wenbo Ma
Jun Xiao
Hanwang Zhang
Shih-Fu Chang

The prevailing framework for solving referring expression grounding is based on a two-stage process: 1) detecting proposals with an object detector and 2) grounding the referent to one of the proposals. Existing two-stage solutions mostly focus on the grounding step, which aims to align the expressions with the proposals. In this paper, we argue that these methods overlook an obvious mismatch between the roles of proposals in the two stages: they generate proposals solely based on the detection confidence (i. e. , expression-agnostic), hoping that the proposals contain all right instances in the expression (i. e. , expression-aware). Due to this mismatch, current two-stage methods suffer from a severe performance drop between detected and ground-truth proposals. To this end, we propose Ref-NMS, which is the first method to yield expression-aware proposals at the first stage. Ref-NMS regards all nouns in the expression as critical objects, and introduces a lightweight module to predict a score for aligning each box with a critical object. These scores can guide the NMS operation to filter out the boxes irrelevant to the expression, increasing the recall of critical objects, resulting in a significantly improved grounding performance. Since Ref- NMS is agnostic to the grounding step, it can be easily integrated into any state-of-the-art two-stage method. Extensive ablation studies on several backbones, benchmarks, and tasks consistently demonstrate the superiority of Ref-NMS. Codes are available at: https: //github. com/ChopinSharp/ref-nms.

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