A practical VEP-based brain-computer interface

Wang, Yijun; Wang, Ruiping; Gao, Xiaorong; Hong, Bo; Gao, Shangkai

doi:10.1109/tnsre.2006.875576

Cited by 541 publications

(310 citation statements)

References 11 publications

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“…An interesting fact to highlight is that the fundamental frequency of each color reached the highest peak of amplitude, which is confirmed by the literature (Herrmann, 2001). Also the frequencies of 13 and 15 Hz reached the highest peaks of amplitude response for both groups, which is confirmed by studies described in the curve of Wang et al (2006), where the middle frequencies (13 and 15 Hz) exceeded in magnitude the low frequencies (8 and 11 Hz).…”

Section: Discussionsupporting

confidence: 81%

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Comparison of the influence of stimuli color on Steady-State Visual Evoked Potentials

et al. 2015

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Introduction:The main idea of a traditional Steady State Visually Evoked Potentials (SSVEP)-BCI is the activation of commands through gaze control. For this purpose, the retina of the eye is excited by a stimulus at a certain frequency. Several studies have shown effects related to different kind of stimuli, frequencies, window lengths, techniques of feature extraction and even classification. So far, none of the previous studies has performed a comparison of performance of stimuli colors through LED technology. This study addresses precisely this important aspect and would be a great contribution to the topic of SSVEP-BCIs. Additionally, the performance of different colors at different frequencies and the visual comfort were evaluated in each case. Methods: LEDs of four different colors (red, green, blue and yellow) flickering at four distinct frequencies (8, 11, 13 and 15 Hz) were used. Twenty subjects were distributed in two groups performing different protocols. Multivariate Synchronization Index (MSI) was the technique adopted as feature extractor. Results: The accuracy was gradually enhanced with the increase of the time window. From our observations, the red color provides, in most frequencies, both highest rates of accuracy and Information Transfer Rate (ITR) for detection of SSVEP. Conclusion:Although the red color has presented higher ITR, this color was turned in the less comfortable one and can even elicit epileptic responses according to the literature. For this reason, the green color is suggested as the best choice according to the proposed rules. In addition, this color has shown to be safe and accurate for an SSVEP-BCI.

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Section: Discussionsupporting

confidence: 81%

“…The SSVEP amplitude response is obtained through PSDA (Power Spectral Density Analysis) method or Fourier Power Analysis (Wang et al, 2006) with Hamming window. SSVEPs response curves were calculated by the following formula: …”

Section: Eeg Pre-processing and Ssvep Response Analysismentioning

confidence: 99%

Comparison of the influence of stimuli color on Steady-State Visual Evoked Potentials

et al. 2015

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“…∑ x |FFT (x)| denotes summing over the total frequency points of the spectrum; thus, the sum of the amplitude spectrum is normalized to one [24]. The results for one subject are shown in Figure 9.…”

Section: Comparison With Ccamentioning

confidence: 99%

Fuzzy Tracking and Control Algorithm for an SSVEP-Based BCI System

Chen

Zaeni

et al. 2016

Applied Sciences

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Subjects with amyotrophic lateral sclerosis (ALS) consistently experience decreasing quality of life because of this distinctive disease. Thus, a practical brain-computer interface (BCI) application can effectively help subjects with ALS to participate in communication or entertainment. In this study, a fuzzy tracking and control algorithm is proposed for developing a BCI remote control system. To represent the characteristics of the measured electroencephalography (EEG) signals after visual stimulation, a fast Fourier transform is applied to extract the EEG features. A self-developed fuzzy tracking algorithm quickly traces the changes of EEG signals. The accuracy and stability of a BCI system can be greatly improved by using a fuzzy control algorithm. Fifteen subjects were asked to attend a performance test of this BCI system. The canonical correlation analysis (CCA) was adopted to compare the proposed approach, and the average recognition rates are 96.97% and 94.49% for proposed approach and CCA, respectively. The experimental results showed that the proposed approach is preferable to CCA. Overall, the proposed fuzzy tracking and control algorithm applied in the BCI system can profoundly help subjects with ALS to control air swimmer drone vehicles for entertainment purposes.

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“…This SNR measure have been used successfully to enhance SSVEP peaks [12]. We used n = 6, so that the frequencies f p ± 0.4 Hz were taken into account.…”

Section: Fourier Power Analysismentioning

confidence: 99%

Steady State Visual Evoked Potentials in the Delta Range (0.5-5 Hz)

Vialatte

Maurice

Dauwels

et al. 2009

Lecture Notes in Computer Science

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Abstract. The usually 'accepted' limits of Steady State Visual Evoked Potentials are in the 3-60 Hz range. Recent studies reported SSVEP activities below 3 Hz, which remains a matter of debate. We recorded EEG responses to stimuli from 0.5 to 13 Hz. We first confirm the possibility to elicit SSVEP below 3 Hz. Afterwards, for the first time, we show that SSVEP recorded in the δ (0.5-5 Hz) range seem to describe several subsystems, with peaks near 1, 2.5 and 5 Hz (close to subharmonics of 10 Hz). Finally, we report surprising results in the lower frequency ranges, with responses for almost all harmonics (e.g. 15 peak responses between 0.5 and 14.5 Hz for stimuli at 1 hz).

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A practical VEP-based brain-computer interface

Cited by 541 publications

References 11 publications

Comparison of the influence of stimuli color on Steady-State Visual Evoked Potentials

Comparison of the influence of stimuli color on Steady-State Visual Evoked Potentials

Fuzzy Tracking and Control Algorithm for an SSVEP-Based BCI System

Steady State Visual Evoked Potentials in the Delta Range (0.5-5 Hz)

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