Brain–Computer Interface Software: A Review and Discussion

Stegman, Pierce; Crawford, Chris; Andujar, Marvin; Nijholt, Anton; Gilbert, Juan E.

doi:10.1109/thms.2020.2968411

Cited by 47 publications

(24 citation statements)

References 97 publications

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“…This opinion is rather qualified in the scientific community, which reported that BCIs suffer from (1) a low transfer rate, (2) a lack of market-ready, affordable, and user-friendly research-grade EEG acquisition devices, and (3) a gap between the game design and graphics of video games available on the market vs. in laboratories. Priorities of these limitations for video game development are discussed in the literature (Nijholt et al, 2009;Ferreira et al, 2013;Marshall et al, 2013;van de Laar et al, 2013;Ahn et al, 2014;Cattan et al, 2018b;Kerous et al, 2018;Vasiljevic and Miranda, 2019;Pierce et al, 2020), and in general preponderance of quantitative over qualitative aspects is criticized (Nijholt et al, 2009;Vasiljevic and Miranda, 2019). This article supports the claim that BCIs are not ready for general public use, based on other aspects than performance.…”

Section: Introductionsupporting

confidence: 60%

“…Another consideration is that any development is materialdependant, as all are based on different hardware, drivers and protocols (e.g., Ahn et al, 2014;Pierce et al, 2020). Despite the lack of standards, commercial brands such as Emotiv (San Francisco, the US) or NeuroSky (San Rose, the US) have developed plugins for game development.…”

Section: Cost and Encumberment Of Materialsmentioning

confidence: 99%

“…For instance, Rosca and Leba (2019) developed a pool game by integrating the Emotiv SDK with Unity 3D (San Francisco, the US), a notorious game engine. A review of existing BCI software can be found in Pierce et al (2020). That said, the lack of middleware supported by a large and independent community creates maintenance and portability issues, as long as plugins are constructor-dependent or rely on the willingness of a research team.…”

Section: Cost and Encumberment Of Materialsmentioning

confidence: 99%

See 2 more Smart Citations

The Use of Brain–Computer Interfaces in Games Is Not Ready for the General Public

Cattan¹

2021

Front. Comput. Sci.

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Section: Introductionsupporting

confidence: 60%

Section: Cost and Encumberment Of Materialsmentioning

confidence: 99%

Section: Cost and Encumberment Of Materialsmentioning

confidence: 99%

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The Use of Brain–Computer Interfaces in Games Is Not Ready for the General Public

Cattan¹

2021

Front. Comput. Sci.

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“…The incorporation of biological signals as control signals could offer individuals suffering from paresis or paralysis a whole new level of independence in their rehabilitation or training regimes. This brain-computer interface is commonly referred to as brain-computer interaction (BCI) [71]. Additionally, more investigation is warranted for studies that not only accommodate but encourage the human's volitional contributions.…”

Section: Discussionmentioning

confidence: 99%

FES Cycling and Closed-Loop Feedback Control for Rehabilitative Human–Robot Interaction

2021

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For individuals with movement impairments due to neurological injuries, rehabilitative therapies such as functional electrical stimulation (FES) and rehabilitation robots hold vast potential to improve their mobility and activities of daily living. Combining FES with rehabilitation robots results in intimately coordinated human–robot interaction. An example of such interaction is FES cycling, where motorized assistance can provide high-intensity and repetitive practice of coordinated limb motion, resulting in physiological and functional benefits. In this paper, the development of multiple FES cycling testbeds and safeguards is described, along with the switched nonlinear dynamics of the cycle–rider system. Closed-loop FES cycling control designs are described for cadence and torque tracking. For each tracking objective, the authors’ past work on robust and adaptive controllers used to compute muscle stimulation and motor current inputs is presented and discussed. Experimental results involving both able-bodied individuals and participants with neurological injuries are provided for each combination of controller and tracking objective. Trade-offs for the control algorithms are discussed based on the requirements for implementation, desired rehabilitation outcomes and resulting rider performance. Lastly, future works and the applicability of the developed methods to additional technologies including teleoperated robotics are outlined.

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“…Brain–computer interfaces (BCIs) provide a direct communication and control channel between the brain and external devices by analyzing neural activity, which has become one of the current study hot spots ( Gao et al, 2014 ; Chen et al, 2015a ; Han et al, 2020 ). Electroencephalogram (EEG) is the most widely used tool for BCIs because of its advantages such as non-invasiveness, low cost, and high temporal resolution ( Stegman et al, 2020 ; Zhang et al, 2020 ). At present, remarkable progresses have been made in the performance and practicability of BCIs due to the optimization of the experimental paradigm, the improvement of the signal processing algorithm, and the application of the machine learning method ( Chen et al, 2015b ; Nakanishi et al, 2018 ; Zhang et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

A Benchmark Dataset for RSVP-Based Brain–Computer Interfaces

Zhang

Wang

Zhang³

et al. 2020

Front. Neurosci.

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This paper reports on a benchmark dataset acquired with a brain–computer interface (BCI) system based on the rapid serial visual presentation (RSVP) paradigm. The dataset consists of 64-channel electroencephalogram (EEG) data from 64 healthy subjects (sub1,…, sub64) while they performed a target image detection task. For each subject, the data contained two groups (“A” and “B”). Each group contained two blocks, and each block included 40 trials that corresponded to 40 stimulus sequences. Each sequence contained 100 images presented at 10 Hz (10 images per second). The stimulus images were street-view images of two categories: target images with human and non-target images without human. Target images were presented randomly in the stimulus sequence with a probability of 1∼4%. During the stimulus presentation, subjects were asked to search for the target images and ignore the non-target images in a subjective manner. To keep all original information, the dataset was the raw continuous data without any processing. On one hand, the dataset can be used as a benchmark dataset to compare the algorithms for target identification in RSVP-based BCIs. On the other hand, the dataset can be used to design new system diagrams and evaluate their BCI performance without collecting any new data through offline simulation. Furthermore, the dataset also provides high-quality data for characterizing and modeling event-related potentials (ERPs) and steady-state visual evoked potentials (SSVEPs) in RSVP-based BCIs. The dataset is freely available from http://bci.med.tsinghua.edu.cn/download.html .

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Brain–Computer Interface Software: A Review and Discussion

Cited by 47 publications

References 97 publications

The Use of Brain–Computer Interfaces in Games Is Not Ready for the General Public

The Use of Brain–Computer Interfaces in Games Is Not Ready for the General Public

FES Cycling and Closed-Loop Feedback Control for Rehabilitative Human–Robot Interaction

A Benchmark Dataset for RSVP-Based Brain–Computer Interfaces

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