Evidence map›Paper›PMID 37680264›Full record

ReviewFrontiers in human neuroscience2023

Non-invasive EEG-based BCI spellers from the beginning to today: a mini-review.

Olga Maslova, Yuliya Komarova, Natalia Shusharina, Alexander Kolsanov, Alexander Zakharov, Evgenia Garina, Vasiliy Pyatin

Abstract readReview
In one paragraph

Review in Frontiers in human neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

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

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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

7 authors.

Olga MaslovaNeurosciences Research Institute, Samara State Medical University, Samara, Russia.
Yuliya KomarovaNeurosciences Research Institute, Samara State Medical University, Samara, Russia.
Natalia ShusharinaBaltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, Kaliningrad, Russia.
Alexander KolsanovDepartment of Operative Surgery and Clinical Anatomy with a Course of Innovative Technologies, Samara State Medical University, Samara, Russia.
Alexander ZakharovNeurosciences Research Institute, Samara State Medical University, Samara, Russia.
Evgenia GarinaDepartment of Physical Culture, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.
Vasiliy PyatinNeurosciences Research Institute, Samara State Medical University, Samara, Russia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The defeat of the central motor neuron leads to the motor disorders. Patients lose the ability to control voluntary muscles, for example, of the upper limbs, which introduces a fundamental dissonance in the possibility of daily use of a computer or smartphone. As a result, the patients lose the ability to communicate with other people. The article presents the most popular paradigms used in the brain-computer-interface speller system and designed for typing by people with severe forms of the movement disorders. Brain-computer interfaces (BCIs) have emerged as a promising technology for individuals with communication impairments. BCI-spellers are systems that enable users to spell words by selecting letters on a computer screen using their brain activity. There are three main types of BCI-spellers: P300, motor imagery (MI), and steady-state visual evoked potential (SSVEP). However, each type has its own limitations, which has led to the development of hybrid BCI-spellers that combine the strengths of multiple types. Hybrid BCI-spellers can improve accuracy and reduce the training period required for users to become proficient. Overall, hybrid BCI-spellers have the potential to improve communication for individuals with impairments by combining the strengths of multiple types of BCI-spellers. In conclusion, BCI-spellers are a promising technology for individuals with communication impairments. P300, MI, and SSVEP are the three main types of BCI-spellers, each with their own advantages and limitations. Further research is needed to improve the accuracy and usability of BCI-spellers and to explore their potential applications in other areas such as gaming and virtual reality.

Indexed as

brain-computer interfacemotor imageryP300people with disabilitiesspeller

Identifiers

PMID37680264
PMCPMC10480564

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.