Evidence map›Paper›PMID 35966991›Full record

ArticleFrontiers in human neuroscience2022

A novel multiple time-frequency sequential coding strategy for hybrid brain-computer interface.

Zan Yue, Qiong Wu, Shi-Yuan Ren, Man Li, Bin Shi, Yu Pan, Jing Wang

Abstract read
In one paragraph

Article in Frontiers in human neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

7 authors.

Zan YueInstitute of Robotics and Intelligent Systems, Xi'an Jiaotong University, Xi'an, China.
Qiong WuBeijing Tsinghua Changgeng Hospital, Tsinghua University, Beijing, China.
Shi-Yuan RenInstitute of Robotics and Intelligent Systems, Xi'an Jiaotong University, Xi'an, China.
Man LiInstitute of Robotics and Intelligent Systems, Xi'an Jiaotong University, Xi'an, China.
Bin ShiInstitute of Robotics and Intelligent Systems, Xi'an Jiaotong University, Xi'an, China.
Yu PanBeijing Tsinghua Changgeng Hospital, Tsinghua University, Beijing, China.
Jing WangInstitute of Robotics and Intelligent Systems, Xi'an Jiaotong University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: For brain-computer interface (BCI) communication, electroencephalography provides a preferable choice due to its high temporal resolution and portability over other neural recording techniques. However, current BCIs are unable to sufficiently use the information from time and frequency domains simultaneously. Thus, we proposed a novel hybrid time-frequency paradigm to investigate better ways of using the time and frequency information. Method: We adopt multiple omitted stimulus potential (OSP) and steady-state motion visual evoked potential (SSMVEP) to design the hybrid paradigm. A series of pre-experiments were undertaken to study factors that would influence the feasibility of the hybrid paradigm and the interaction between multiple features. After that, a novel Multiple Time-Frequencies Sequential Coding (MTFSC) strategy was introduced and explored in experiments. Results: Omissions with multiple short and long durations could effectively elicit time and frequency features, including the multi-OSP, ERP, and SSVEP in this hybrid paradigm. The MTFSC was feasible and efficient. The preliminary online analysis showed that the accuracy and the ITR of the nine-target stimulator over thirteen subjects were 89.04% and 36.37 bits/min. Significance: This study first combined the SSMVEP and multi-OSP in a hybrid paradigm to produce robust and abundant time features for coding BCI. Meanwhile, the MTFSC proved feasible and showed great potential in improving performance, such as expanding the number of BCI targets by better using time information in specific stimulated frequencies. This study holds promise for designing better BCI systems with a novel coding method.

Indexed as

event-related potential (ERP)hybrid brain-computer interfacemultiple time-frequencies sequential codingomitted stimulus potentialsteady-state visual evoked potentials

Identifiers

PMID35966991
PMCPMC9372511

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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.