Curated Papers > High-impact authoritative literature on "brain-computer interfaces"

Brain-Computer Interface Spellers: A Review.

Literature Information

DOI	10.3390/brainsci8040057
PMID	29601538
Journal	Brain sciences
Impact Factor	2.8
JCR Quartile	Q3
Publication Year	2018
Times Cited	98
Keywords	Brain–Computer Interface (BCI), Graphical User Interface (GUI), MI, P300, SSVEP
Literature Type	Journal Article, Review
ISSN	2076-3425
Issue	8(4)
Authors	Aya Rezeika, Mihaly Benda, Piotr Stawicki, Felix Gembler, Abdul Saboor, Ivan Volosyak

TL;DR

This review consolidates and categorizes various Brain-Computer Interface (BCI) spellers developed since 2010, highlighting their underlying paradigms such as P300, SSVEP, and motor imagery, and aims to assist researchers by providing a comprehensive overview of their features and graphical user interfaces. By offering a taxonomy of these systems, the study facilitates the identification of suitable BCI-spellers for users and highlights opportunities for further development in the field.

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Brain–Computer Interface (BCI) · Graphical User Interface (GUI) · MI · P300 · SSVEP

Abstract

A Brain-Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field. Although many BCI-spellers have been developed during the last few decades, to our knowledge, no reviews have described the different spellers proposed and studied in this vital field. The presented speller systems are categorized according to major BCI paradigms: P300, steady-state visual evoked potential (SSVEP), and motor imagery (MI). Different BCI paradigms require specific electroencephalogram (EEG) signal features and lead to the development of appropriate Graphical User Interfaces (GUIs). The purpose of this review is to consolidate the most successful BCI-spellers published since 2010, while mentioning some other older systems which were built explicitly for spelling purposes. We aim to assist researchers and concerned individuals in the field by illustrating the highlights of different spellers and presenting them in one review. It is almost impossible to carry out an objective comparison between different spellers, as each has its variables, parameters, and conditions. However, the gathered information and the provided taxonomy about different BCI-spellers can be helpful, as it could identify suitable systems for first-hand users, as well as opportunities of development and learning from previous studies for BCI researchers.

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Primary Questions Addressed

1. What are the key challenges faced in the development of BCI-spellers using different paradigms like P300 and SSVEP?
2. How do the user interface designs of various BCI-spellers impact user experience and efficiency in communication?
3. In what ways can the taxonomy of BCI-spellers help guide future research and development in the field?
4. What advancements in EEG signal processing have contributed to the effectiveness of modern BCI-spellers?
5. How do different populations (e.g., individuals with disabilities vs. healthy users) respond to BCI-spellers, and what implications does this have for system design?

Key Findings

Research Background and Purpose

Brain-Computer Interfaces (BCIs) provide a non-muscular communication method through brain signals, with BCI spellers being one of the earliest applications. This review consolidates advancements in BCI spellers developed since 2010, categorizing them based on BCI paradigms such as P300, Steady-State Visual Evoked Potential (SSVEP), and Motor Imagery (MI). The objective is to present a comprehensive overview that aids researchers and users in identifying effective systems and opportunities for further development.

Main Methods/Materials/Experimental Design

The review employs a systematic literature search following PRISMA guidelines, focusing on studies published between 2010 and January 2018. The search utilized keywords related to BCI and spellers across databases like IEEE Xplore and Web of Science. Papers were filtered based on criteria including the type of BCI (non-invasive, EEG-based), stimulus type, and relevance to GUI development.

Key Results and Findings

• Taxonomy of BCI Spellers: The review categorizes 69 speller systems, with 65% based on P300, 23% on SSVEP, and 6% on MI.
• Performance Metrics: Performance is typically evaluated by accuracy and Information Transfer Rate (ITR). P300 spellers showed varied performance based on design and user interface modifications.
• GUI Innovations: Various graphical user interface designs, such as matrix spellers, familiar face stimuli, and prediction modules, have been explored to enhance user experience and efficiency.

Main Conclusions/Significance/Innovation

The review highlights the importance of GUI design in BCI spellers, noting that the user interface is the first point of interaction for users. It emphasizes that while P300 spellers are popular due to their high ITR, they face challenges like slow performance with increased commands. Innovations such as hybrid systems combining P300 and SSVEP or utilizing MI are noted for their potential to enhance speed and usability. The findings underscore the need for continued research and development to improve BCI spellers for practical use, especially among patients with severe disabilities.

Research Limitations and Future Directions

• Limitations: The review notes the difficulty in objectively comparing spellers due to varying methodologies, hardware, and performance metrics across studies. Limited testing on patients compared to healthy subjects also restricts the applicability of findings.
• Future Directions: Further research should focus on enhancing BCI speller designs, improving training protocols, and expanding testing with diverse user groups, particularly those with motor impairments. There is also potential in developing gaze-independent spellers and integrating multimodal sensory feedback to improve user interaction and performance.

Summary Table of BCI Speller Systems

BCI Paradigm	Number of Studies	Main Features	Average ITR	Accuracy
P300	45	Matrix spellers, familiar face stimuli	32.71 bits/min	93.27%
SSVEP	16	High-speed selection, gaze-independent	61.64 bits/min	98.78%
MI	4	Motor imagery control, asynchronous	27.36 bits/min	85%
Hybrid	4	Combination of P300 and SSVEP	90.9 bits/min	92.93%

This structured summary provides an overview of the advancements and challenges in BCI spellers, emphasizing the significance of user interface design and the need for continued innovation in the field.

References

1. Use of a Green Familiar Faces Paradigm Improves P300-Speller Brain-Computer Interface Performance. - Qi Li;Shuai Liu;Jian Li;Ou Bai - PloS one (2015)
2. An Auditory-Tactile Visual Saccade-Independent P300 Brain-Computer Interface. - Erwei Yin;Timothy Zeyl;Rami Saab;Dewen Hu;Zongtan Zhou;Tom Chau - International journal of neural systems (2016)
3. Brain-computer interface technology: a review of the first international meeting. - J R Wolpaw;N Birbaumer;W J Heetderks;D J McFarland;P H Peckham;G Schalk;E Donchin;L A Quatrano;C J Robinson;T M Vaughan - IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society (2000)
4. A Dynamically Optimized SSVEP Brain-Computer Interface (BCI) Speller. - Erwei Yin;Zongtan Zhou;Jun Jiang;Yang Yu;Dewen Hu - IEEE transactions on bio-medical engineering (2015)
5. A Hybrid Brain-Computer Interface Based on the Fusion of P300 and SSVEP Scores. - Erwei Yin;Timothy Zeyl;Rami Saab;Tom Chau;Dewen Hu;Zongtan Zhou - IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society (2015)
6. Brain-computer interface using water-based electrodes. - Ivan Volosyak;Diana Valbuena;Tatsiana Malechka;Jan Peuscher;Axel Gräser - Journal of neural engineering (2010)
7. Training leads to increased auditory brain-computer interface performance of end-users with motor impairments. - S Halder;I Käthner;A Kübler - Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology (2016)
8. Assistive device with conventional, alternative, and brain-computer interface inputs to enhance interaction with the environment for people with amyotrophic lateral sclerosis: a feasibility and usability study. - Francesca Schettini;Angela Riccio;Luca Simione;Giulia Liberati;Mario Caruso;Vittorio Frasca;Barbara Calabrese;Massimo Mecella;Alessia Pizzimenti;Maurizio Inghilleri;Donatella Mattia;Febo Cincotti - Archives of physical medicine and rehabilitation (2015)
9. New stimulation pattern design to improve P300-based matrix speller performance at high flash rate. - Chantri Polprasert;Pratana Kukieattikool;Tanee Demeechai;James A Ritcey;Siwaruk Siwamogsatham - Journal of neural engineering (2013)
10. Predictive spelling with a P300-based brain-computer interface: Increasing the rate of communication. - D B Ryan;G E Frye;G Townsend;D R Berry;S Mesa-G;N A Gates;E W Sellers - International journal of human-computer interaction (2011)

Literatures Citing This Work

1. A 20-Questions-Based Binary Spelling Interface for Communication Systems. - Alessandro Tonin;Niels Birbaumer;Ujwal Chaudhary - Brain sciences (2018)
2. A New Frontier: The Convergence of Nanotechnology, Brain Machine Interfaces, and Artificial Intelligence. - Gabriel A Silva - Frontiers in neuroscience (2018)
3. Brain⁻Computer Interfaces for Human Augmentation. - Davide Valeriani;Caterina Cinel;Riccardo Poli - Brain sciences (2019)
4. Neurotechnologies for Human Cognitive Augmentation: Current State of the Art and Future Prospects. - Caterina Cinel;Davide Valeriani;Riccardo Poli - Frontiers in human neuroscience (2019)
5. Asynchronous non-invasive high-speed BCI speller with robust non-control state detection. - Sebastian Nagel;Martin Spüler - Scientific reports (2019)
6. Dynamic time window mechanism for time synchronous VEP-based BCIs-Performance evaluation with a dictionary-supported BCI speller employing SSVEP and c-VEP. - Felix Gembler;Piotr Stawicki;Abdul Saboor;Ivan Volosyak - PloS one (2019)
7. Vigilance state fluctuations and performance using brain-computer interface for communication. - Barry Oken;Tab Memmott;Brandon Eddy;Jack Wiedrick;Melanie Fried-Oken - Brain computer interfaces (Abingdon, England) (2018)
8. Impact of Speller Size on a Visual P300 Brain-Computer Interface (BCI) System under Two Conditions of Constraint for Eye Movement. - R Ron-Angevin;L Garcia;Á Fernández-Rodríguez;J Saracco;J M André;V Lespinet-Najib - Computational intelligence and neuroscience (2019)
9. User Experience of 7 Mobile Electroencephalography Devices: Comparative Study. - Thea Radüntz;Beate Meffert - JMIR mHealth and uHealth (2019)
10. Happy emotion cognition of bimodal audiovisual stimuli optimizes the performance of the P300 speller. - Zhaohua Lu;Qi Li;Ning Gao;Jingjing Yang;Ou Bai - Brain and behavior (2019)

... (88 more literatures)

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