Development of a hybrid mental spelling system combining SSVEP-based brain–computer interface and webcam-based eye tracking

Lim, Jung Min; Lee, Jun Hak; Hwang, Han-Jeong; Kim, D.H.; Im, Chang‐Hwan

doi:10.1016/j.bspc.2015.05.012

Cited by 56 publications

(38 citation statements)

References 55 publications

(89 reference statements)

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“…The separation of the keyboard and stimuli also makes FlashType TM invariant to the alphabet and language. Although, FlashType TM uses a cursor based hierarchical selection method, due to the high accuracy of every action, Character Suggestions and Word Predictions, the average required time per character is shorter or comparable to that of similar systems on one of tests like those reported here [25]. The hierarchical decision making mechanism also makes the system more robust to errors.…”

Section: Discussionmentioning

confidence: 56%

FlashType<inline-formula> <tex-math notation="LaTeX">$^{\text{TM}}$</tex-math> </inline-formula>: A Context-Aware c-VEP-Based BCI Typing Interface Using EEG Signals

Nezamfar

Salehi

Moghadamfalahi

et al. 2016

IEEE J. Sel. Top. Signal Process.

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Brain computer interfaces (BCIs) offer individuals with disabilities an alternative channel of communication and control, hence they have been receiving increasing interest. BCIs can also be useful for healthy individuals in situations limiting their movement or where other computer interaction modalities need to be supplemented. Event-related and steady state visually evoked potentials (SSVEPs) are the top two brain signal types used in developing BCIs that allow the user to make a choice from a discrete set of options, including the selection of commands from a menu for a robot or computer to perform, as well as typing letters, symbols, or icons for communication. Popular BCI speller paradigms, such as the P300 Matrix Speller, RSVP Keyboard TM , or SSVEP spellers in which the letters on the keyboard display flicker, are sensitive to the font, size and presentation speed. In addition, sensitivity to eye gaze control plays a significant role in usability of most of these keyboards.We present a code-VEP based BCI, utilized in a language model assisted keyboard application. Utilizing a cursor based selection method, stimuli and targets are separated. FlashType TM separates visual stimulation from alphabet presentation to achieve performance invariance under presentation variations. Therefore, FlashType TM can be used for all languages, including the ones containing symbols and icons. FlashType TM , contains a Static Keyboard, a row of Suggested Characters and a row of Predicted Words. FlashType TM , by default, uses only one EEG electrode and four stimuli. The system can operate using only one stimulus at a lower selection rate, useful for individuals with limited or no gaze control. This feature is to be explored in future. Replacing letters with text or icons representing commands would allow controlling a computer or robot. In this study, FlashType TM has been evaluated by three individuals performing 10 Mastery tasks. In depth experimentation, such as assessing the system with potential end users writing long passages of text, will be done in future.

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Section: Discussionmentioning

confidence: 56%

FlashType<inline-formula> <tex-math notation="LaTeX">$^{\text{TM}}$</tex-math> </inline-formula>: A Context-Aware c-VEP-Based BCI Typing Interface Using EEG Signals

Nezamfar

Salehi

Moghadamfalahi

et al. 2016

IEEE J. Sel. Top. Signal Process.

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“…One may argue that the Eyelink used in this study is also a research grade eye tracking system and can achieve good accuracy by itself. However, we argue on the basis of previous studies that have used cheap cameras for eye tracking and have shown that eye tracking classifications are low if the targets are densely located with small sizes and SSVEP-based BCI performs better in such a scenario [65,81]. On the other hand, there are higher chances of misclassification of targets in SSVEP-based BCIs when decoding a large number of targets.…”

Section: Discussionmentioning

confidence: 88%

“…In this study, the proposed hybrid approach was also compared with a previously developed hybrid mental spelling system [81]. The basic idea of [81] was to divide the speller into three parts, i.e., left, middle, and right. In this sense, the misclassification of the SSVEPs could be reduced to improve the classification accuracies and ITR of the system.…”

Section: Hybrid Eeg-eye Tackingmentioning

confidence: 99%

“…They also showed that an SSVEP-based BCI performed better than an eye tracking-based speller when targets are densely located and small in size. Recently, eye tracking has been combined with EEG to remove ocular artifacts from EEG signals [74,75] and to develop hybrid BCI systems [65,[76][77][78][79][80][81]. All these studies have shown improved performance as compared to EEG only, as well as the feasibility of combining EEG with eye tracking.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Speller Design Using Eye Tracking and SSVEP Brain–Computer Interface

Mannan

Kamran

Kang

et al. 2020

Sensors

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Steady-state visual evoked potentials (SSVEPs) have been extensively utilized to develop brain–computer interfaces (BCIs) due to the advantages of robustness, large number of commands, high classification accuracies, and information transfer rates (ITRs). However, the use of several simultaneous flickering stimuli often causes high levels of user discomfort, tiredness, annoyingness, and fatigue. Here we propose to design a stimuli-responsive hybrid speller by using electroencephalography (EEG) and video-based eye-tracking to increase user comfortability levels when presented with large numbers of simultaneously flickering stimuli. Interestingly, a canonical correlation analysis (CCA)-based framework was useful to identify target frequency with a 1 s duration of flickering signal. Our proposed BCI-speller uses only six frequencies to classify forty-eight targets, thus achieve greatly increased ITR, whereas basic SSVEP BCI-spellers use an equal number of frequencies to the number of targets. Using this speller, we obtained an average classification accuracy of 90.35 ± 3.597% with an average ITR of 184.06 ± 12.761 bits per minute in a cued-spelling task and an ITR of 190.73 ± 17.849 bits per minute in a free-spelling task. Consequently, our proposed speller is superior to the other spellers in terms of targets classified, classification accuracy, and ITR, while producing less fatigue, annoyingness, tiredness and discomfort. Together, our proposed hybrid eye tracking and SSVEP BCI-based system will ultimately enable a truly high-speed communication channel.

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“…Over the last couple of decades, promising and successful BCI applications have been proposed [23], and their feasibility has been proved through validation experiments [24]. A mental speller is one of the most popular BCI applications, which allows LIS patients to communicate a message [7,[25][26][27][28]. In addition to a mental speller, different BCI applications have been developed, such as a robotic arm [29][30][31], mobile robot [32], wheelchair [33][34][35][36][37][38], mouse [39,40], smart home system [41], mobile phone application [42], and various games [43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Brain-Switches for Asynchronous Brain–Computer Interfaces: A Systematic Review

2020

Self Cite

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A brain–computer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever the user wants. However, the relatively low performance of an asynchronous BCI system is problematic because redundant BCI commands are required to correct false-positive operations. To significantly reduce the number of false-positive operations of an asynchronous BCI system, a two-step approach has been proposed using a brain-switch that first determines whether the user wants to use an asynchronous BCI system before the operation of the asynchronous BCI system. This study presents a systematic review of the state-of-the-art brain-switch techniques and future research directions. To this end, we reviewed brain-switch research articles published from 2000 to 2019 in terms of their (a) neuroimaging modality, (b) paradigm, (c) operation algorithm, and (d) performance.

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Development of a hybrid mental spelling system combining SSVEP-based brain–computer interface and webcam-based eye tracking

Cited by 56 publications

References 55 publications

FlashType<inline-formula> <tex-math notation="LaTeX">$^{\text{TM}}$</tex-math> </inline-formula>: A Context-Aware c-VEP-Based BCI Typing Interface Using EEG Signals

FlashType<inline-formula> <tex-math notation="LaTeX">$^{\text{TM}}$</tex-math> </inline-formula>: A Context-Aware c-VEP-Based BCI Typing Interface Using EEG Signals

A Hybrid Speller Design Using Eye Tracking and SSVEP Brain–Computer Interface

Brain-Switches for Asynchronous Brain–Computer Interfaces: A Systematic Review

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