Evidence map›Paper›PMID 28207382›Full record

ArticleIEEE transactions on bio-medical engineering2017

Performance Assessment of a Custom, Portable, and Low-Cost Brain-Computer Interface Platform.

Colin M McCrimmon, Jonathan Lee Fu, Ming Wang, Lucas Silva Lopes, Po T Wang, Alireza Karimi-Bidhendi, Charles Y Liu, Payam Heydari, Zoran Nenadic, An Hong Do

Abstract readEvaluation Study
In one paragraph

Article in IEEE transactions on bio-medical engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–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

13 citing papers in PubMed.

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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

10 authors.

Colin M McCrimmon
Jonathan Lee Fu
Ming Wang
Lucas Silva Lopes
Po T Wang
Alireza Karimi-Bidhendi
Charles Y Liu
Payam Heydari
Zoran Nenadic
An Hong Do

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM008620 · NIGMS · UNIVERSITY OF CALIFORNIA-IRVINE · PI GOLDIN, ALAN L · 1999 to 2023
$8.1M
NIGMS NIH HHS T32 GM008620
6 · The paper itself

Abstract

objectiveConventional brain-computer interfaces (BCIs) are often expensive, complex to operate, and lack portability, which confines their use to laboratory settings. Portable, inexpensive BCIs can mitigate these problems, but it remains unclear whether their low-cost design compromises their performance. Therefore, we developed a portable, low-cost BCI and compared its performance to that of a conventional BCI.

methodsThe BCI was assembled by integrating a custom electroencephalogram (EEG) amplifier with an open-source microcontroller and a touchscreen. The function of the amplifier was first validated against a commercial bioamplifier, followed by a head-to-head comparison between the custom BCI (using four EEG channels) and a conventional 32-channel BCI. Specifically, five able-bodied subjects were cued to alternate between hand opening/closing and remaining motionless while the BCI decoded their movement state in real time and provided visual feedback through a light emitting diode. Subjects repeated the above task for a total of 10 trials, and were unaware of which system was being used. The performance in each trial was defined as the temporal correlation between the cues and the decoded states.

resultsThe EEG data simultaneously acquired with the custom and commercial amplifiers were visually similar and highly correlated ( ρ = 0.79). The decoding performances of the custom and conventional BCIs averaged across trials and subjects were 0.70 ± 0.12 and 0.68 ± 0.10, respectively, and were not significantly different.

conclusionThe performance of our portable, low-cost BCI is comparable to that of the conventional BCIs. SIGNIFICANCE: Platforms, such as the one developed here, are suitable for BCI applications outside of a laboratory.

Indexed as

User-Computer InterfaceAmplifiers, ElectronicBrain-Computer InterfacesBrain MappingCost-Benefit AnalysisEquipment DesignEquipment Failure AnalysisEvoked PotentialsHumansMiniaturizationReproducibility of ResultsSensitivity and SpecificityUnited States

Identifiers

PMID28207382
PMCPMC6508950

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Registered trials

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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.