Evidence map›Paper›PMID 40253415›Full record

ArticleScientific data2025

VocalMind: A Stereotactic EEG Dataset for Vocalized, Mimed, and Imagined Speech in Tonal Language.

Tianyu He, Mingyi Wei, Ruicong Wang, Renzhi Wang, Shiwei Du, Siqi Cai, Wei Tao, Haizhou Li

Abstract readDataset
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Tianyu He *School of Data Science, Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, P. R. China.
Mingyi Wei *Department of Neurosurgery, South China Hospital, Medical School, Shenzhen University, Shenzhen, 518116, P. R. China.
Ruicong WangSchool of Data Science, Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, P. R. China.
Renzhi WangSchool of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, P. R. China.
Shiwei DuDepartment of Neurosurgery, South China Hospital, Medical School, Shenzhen University, Shenzhen, 518116, P. R. China.
Siqi CaiSchool of Data Science, Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, P. R. China. elesiqi@nus.edu.sg.ORCID http://orcid.org/0000-0003-3282-9246
Wei TaoDepartment of Neurosurgery, South China Hospital, Medical School, Shenzhen University, Shenzhen, 518116, P. R. China. taowmail@163.com.
Haizhou LiSchool of Data Science, Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, P. R. China. haizhouli@cuhk.edu.cn.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62271432
6 · The paper itself

Abstract

Speech BCIs based on implanted electrodes hold significant promise for enhancing spoken communication through high temporal resolution and invasive neural sensing. Despite the potential, acquiring such data is challenging due to its invasive nature, and publicly available datasets, particularly for tonal languages, are limited. In this study, we introduce VocalMind, a stereotactic electroencephalography (sEEG) dataset focused on Mandarin Chinese, a tonal language. This dataset includes sEEG-speech parallel recordings from three distinct speech modes, namely vocalized speech, mimed speech, and imagined speech, at both word and sentence levels, totaling over one hour of intracranial neural recordings related to speech production. This paper also presents a baseline model as the reference model for future studies, at the same time, ensuring the integrity of the dataset. The diversity of tasks and the substantial data volume provide a valuable resource for developing advanced algorithms for speech decoding, thereby advancing BCI research for spoken communication.

Indexed as

ElectroencephalographyLanguageSpeechBrain-Computer InterfacesHumans

Identifiers

PMID40253415
PMCPMC12009324

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.