Brain-computer interface based on high frequency steady-state visual evoked potentials: A feasibility study

Hoffmann, Ulrich; Fimbel, Eric; Keller, Thierry

doi:10.1109/ner.2009.5109334

Cited by 14 publications

(13 citation statements)

References 13 publications

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“…[26] Additionally, our proposed SSVEP speller, using high-frequency stimulation, also resulted in high performance. In comparison with previous studies on high-frequency stimulation, [27,29,31,45] the classification accuracy and ITR of our SSVEP speller were much better. Furthermore, the frequency resolution with the high-frequency stimulus provided by our speller was 0.2 Hz, the highest SSVEP frequency resolution provided as far.…”

Section: Stimulus Frequencycontrasting

confidence: 72%

“…A probable solution addressing this problem is to adopt high-frequency stimulation; however, this has received little attention so far. [26][27][28][29]31] Researchers might be concerned that high-frequency SSVEP would be too weak to detect. However, in this study we found that the SNR curves of SSVEP were similar in the low-and high-frequency ranges, which was consistent with a previous study.…”

Section: Stimulus Frequencymentioning

confidence: 99%

“…However, SSVEPs induced by high-frequency stimuli have specific advantages such as a great decrease in the user's fatigue and risk of photosensitive epileptic seizures. [26][27][28][29][30][31][32] Given the respective advantages of low-and high-frequency stimuli, SSVEPs induced by both low-and high-frequency stimuli were employed as the communication medium respectively in this study.…”

Section: Introductionmentioning

confidence: 99%

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A high-ITR SSVEP-based BCI speller

Chen

Gao

et al. 2014

Brain-Computer Interfaces

249

196

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Spelling is an important application of brain-computer interfaces (BCIs). Previous BCI spellers were not suited for widespread use due to their low information transfer rate (ITR). In this study, we constructed a high-ITR BCI speller based on the steady-state visual evoked potential (SSVEP). A 45-target BCI speller was implemented with a frequency resolution of 0.2 Hz. A sampled sinusoidal stimulation method was used to present visual stimuli on a conventional LCD screen. The online results revealed that the proposed BCI speller had a good performance, reaching a high average accuracy (84.1% for 2 s stimulation time; 90.2% for 3 s stimulation time) and the corresponding high ITR (105 bits/min for 2 s stimulation time, 82 bits/min for 3 s stimulation time) during the low-frequency stimuli, while 88.7% and 61 bits/ min were achieved for a 4 s time window during the high-frequency stimuli.

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Section: Stimulus Frequencycontrasting

confidence: 72%

Section: Stimulus Frequencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A high-ITR SSVEP-based BCI speller

Chen

Gao

et al. 2014

Brain-Computer Interfaces

249

196

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“…The system achieved an ITR of 20.13 bits/min, with a control state classification accuracy of more than 97%. Higher frequency flicker could be used to increase the user's comfort when using the system [19]. This would necessitate sophisticated hardware and detection algorithms.…”

Section: Discussionmentioning

confidence: 99%

An Asynchronous P300 BCI With SSVEP-Based Control State Detection

Panicker

Puthusserypady

Sun

2011

IEEE Trans. Biomed. Eng.

183

129

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An asynchronous hybrid brain-computer interface (BCI) system combining the P300 and steady-state visually evoked potentials (SSVEP) paradigms is introduced. A P300 base system is used for information transfer, and is augmented to include SSVEP for control state detection. The proposed system has been validated through off-line and online experiments. It is shown to achieve fast and accurate control state detection without significantly compromising the performance. For the two subjects who participated in the online experiments, the system achieved an average data transfer rate of 20.13 bits/min, with control state classification accuracy of more than 97%.

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“…Despite remarkable advancements in both neuroscience, signal processing algorithms and portable computing devices [1], [2], the promise of a practical, user-friendly, noninvasive, and mobile EEG-based BCI platform has remained elusive. Conventional BCI systems have always relied on laboratory bound instrumentation [3]- [7] and often require extensive subject preparation, including scalp abrasion, gels and a multitude of wired electrodes. Thus, the unassisted use of EEG-based BCI systems, outside the laboratory, is still a difficult proposition.…”

Section: Introductionmentioning

confidence: 99%

Dry and Noncontact EEG Sensors for Mobile Brain–Computer Interfaces

Wang

et al. 2012

IEEE Trans. Neural Syst. Rehabil. Eng.

257

152

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Abstract-Dry and noncontact electroencephalographic (EEG) electrodes, which do not require gel or even direct scalp coupling, have been considered as an enabler of practical, real-world, brain-computer interface (BCI) platforms. This study compares wet electrodes to dry and through hair, noncontact electrodes within a steady state visual evoked potential (SSVEP) BCI paradigm. The construction of a dry contact electrode, featuring fingered contact posts and active buffering circuitry is presented. Additionally, the development of a new, noncontact, capacitive electrode that utilizes a custom integrated, high-impedance analog front-end is introduced. Offline tests on 10 subjects characterize the signal quality from the different electrodes and demonstrate that acquisition of small amplitude, SSVEP signals is possible, even through hair using the new integrated noncontact sensor. Online BCI experiments demonstrate that the information transfer rate (ITR) with the dry electrodes is comparable to that of wet electrodes, completely without the need for gel or other conductive media. In addition, data from the noncontact electrode, operating on the top of hair, show a maximum ITR in excess of 19 bits/min at 100% accuracy (versus 29.2 bits/min for wet electrodes and 34.4 bits/min for dry electrodes), a level that has never been demonstrated before. The results of these experiments show that both dry and noncontact electrodes, with further development, may become a viable tool for both future mobile BCI and general EEG applications.Index Terms-Brain-computer interface (BCI), capacitive electrodes, dry electrodes, electroencephalographic (EEG), noncontact electrodes, steady state visual evoked potential (SSVEP).

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Brain-computer interface based on high frequency steady-state visual evoked potentials: A feasibility study

Cited by 14 publications

References 13 publications

A high-ITR SSVEP-based BCI speller

A high-ITR SSVEP-based BCI speller

An Asynchronous P300 BCI With SSVEP-Based Control State Detection

Dry and Noncontact EEG Sensors for Mobile Brain–Computer Interfaces

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