Development of a practical high frequency brain–computer interface based on steady-state visual evoked potentials using a single channel of EEG

Ajami, Saba; Mahnam, Amin; Abootalebi, Vahid

doi:10.1016/j.bbe.2017.10.004

Cited by 43 publications

(29 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the setup process is tedious (attaching the electrodes, adjusting the impedance) and participants find the system uncomfortable. In recent years, research has been conducted into use of monochannel EEG systems in brain-computer interfaces (BCI´s) [30] (analyzed band: 0.5-10 Hz), [31] (steady-state visual evoked potentials), sleep studies [32,33], etc. However, to our knowledge, a single-channel system for detecting activity in the gamma band has not been implemented in imaginary movements.…”

Section: Introductionmentioning

confidence: 99%

Induced Gamma-Band Activity during Actual and Imaginary Movements: EEG Analysis

Usanos

Boquete

Santiago

et al. 2020

Sensors

View full text Add to dashboard Cite

The purpose of this paper is to record and analyze induced gamma-band activity (GBA) (30–60 Hz) in cerebral motor areas during imaginary movement and to compare it quantitatively with activity recorded in the same areas during actual movement using a simplified electroencephalogram (EEG). Brain activity (basal activity, imaginary motor task and actual motor task) is obtained from 12 healthy volunteer subjects using an EEG (Cz channel). GBA is analyzed using the mean power spectral density (PSD) value. Event-related synchronization (ERS) is calculated from the PSD values of the basal GBA (GBAb), the GBA of the imaginary movement (GBAim) and the GBA of the actual movement (GBAac). The mean GBAim and GBAac values for the right and left hands are significantly higher than the GBAb value (p = 0.007). No significant difference is detected between mean GBA values during the imaginary and actual movement (p = 0.242). The mean ERS values for the imaginary movement (ERSimM (%) = 23.52) and for the actual movement (ERSacM = 27.47) do not present any significant difference (p = 0.117). We demonstrated that ERS could provide a useful way of indirectly checking the function of neuronal motor circuits activated by voluntary movement, both imaginary and actual. These results, as a proof of concept, could be applied to physiology studies, brain–computer interfaces, and diagnosis of cognitive or motor pathologies.

show abstract

Section: Introductionmentioning

confidence: 99%

Induced Gamma-Band Activity during Actual and Imaginary Movements: EEG Analysis

Usanos

Boquete

Santiago

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Much research has been devoted to solving these problems. Polychromatic SSVEP 15 or high-frequency SSVEP 16 has been proposed to enhance comfort. However, all of these SSVEP methods are based on the perception of property changes in color space, flicker always exists, and the application scope is narrow.…”

Section: Introductionmentioning

confidence: 99%

Highly Interactive Brain–Computer Interface Based on Flicker-Free Steady-State Motion Visual Evoked Potential

Han

Xie

et al. 2018

Sci Rep

View full text Add to dashboard Cite

Visual evoked potential-based brain–computer interfaces (BCIs) have been widely investigated because of their easy system configuration and high information transfer rate (ITR). However, the uncomfortable flicker or brightness modulation of existing methods restricts the practical interactivity of BCI applications. In our study, a flicker-free steady-state motion visual evoked potential (FF-SSMVEP)-based BCI was proposed. Ring-shaped motion checkerboard patterns with oscillating expansion and contraction motions were presented by a high-refresh-rate display for visual stimuli, and the brightness of the stimuli was kept constant. Compared with SSVEPs, few harmonic responses were elicited by FF-SSMVEPs, and the frequency energy of SSMVEPs was concentrative. These FF-SSMVEPs evoked “single fundamental peak” responses after signal processing without harmonic and subharmonic peaks. More stimulation frequencies could thus be selected to elicit more responding fundamental peaks without overlap with harmonic peaks. A 40-target online SSMVEP-based BCI system was achieved that provided an ITR up to 1.52 bits per second (91.2 bits/min), and user training was not required to use this system. This study also demonstrated that the FF-SSMVEP-based BCI system has low contrast and low visual fatigue, offering a better alternative to conventional SSVEP-based BCIs.

show abstract

“…However, these are associated with subjective fatigue and discomfort and higher risk of photosensitive epileptic seizures (Pastor et al, 2003 ; Duszyk et al, 2014 ). On the other hand, high-frequency (25–50 Hz) stimulus range yield lower amplitude in SSVEP (Diez et al, 2011 ; Ajami et al, 2018 ); however, they give stable subjective performance with the passage of time making them suitable for SSVEP-based BCI (Won et al, 2015 ). Several studies have utilized low to medium range frequencies as these have higher SNR than high frequency range and are easy to detect (Friman et al, 2007 ; Muller et al, 2008 ; Ortner et al, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Use of Sine Shaped High-Frequency Rhythmic Visual Stimuli Patterns for SSVEP Response Analysis and Fatigue Rate Evaluation in Normal Subjects

Keihani

Shirzhiyan

Farahi

et al. 2018

Front. Hum. Neurosci.

Self Cite

View full text Add to dashboard Cite

Background: Recent EEG-SSVEP signal based BCI studies have used high frequency square pulse visual stimuli to reduce subjective fatigue. However, the effect of total harmonic distortion (THD) has not been considered. Compared to CRT and LCD monitors, LED screen displays high-frequency wave with better refresh rate. In this study, we present high frequency sine wave simple and rhythmic patterns with low THD rate by LED to analyze SSVEP responses and evaluate subjective fatigue in normal subjects.Materials and Methods: We used patterns of 3-sequence high-frequency sine waves (25, 30, and 35 Hz) to design our visual stimuli. Nine stimuli patterns, 3 simple (repetition of each of above 3 frequencies e.g., P25-25-25) and 6 rhythmic (all of the frequencies in 6 different sequences e.g., P25-30-35) were chosen. A hardware setup with low THD rate (<0.1%) was designed to present these patterns on LED. Twenty two normal subjects (aged 23–30 (25 ± 2.1) yrs) were enrolled. Visual analog scale (VAS) was used for subjective fatigue evaluation after presentation of each stimulus pattern. PSD, CCA, and LASSO methods were employed to analyze SSVEP responses. The data including SSVEP features and fatigue rate for different visual stimuli patterns were statistically evaluated.Results: All 9 visual stimuli patterns elicited SSVEP responses. Overall, obtained accuracy rates were 88.35% for PSD and > 90% for CCA and LASSO (for TWs > 1 s). High frequency rhythmic patterns group with low THD rate showed higher accuracy rate (99.24%) than simple patterns group (98.48%). Repeated measure ANOVA showed significant difference between rhythmic pattern features (P < 0.0005). Overall, there was no significant difference between the VAS of rhythmic [3.85 ± 2.13] compared to the simple patterns group [3.96 ± 2.21], (P = 0.63). Rhythmic group had lower within group VAS variation (min = P25-30-35 [2.90 ± 2.45], max = P35-25-30 [4.81 ± 2.65]) as well as least individual pattern VAS (P25-30-35).Discussion and Conclusion: Overall, rhythmic and simple pattern groups had higher and similar accuracy rates. Rhythmic stimuli patterns showed insignificantly lower fatigue rate than simple patterns. We conclude that both rhythmic and simple visual high frequency sine wave stimuli require further research for human subject SSVEP-BCI studies.

show abstract

Development of a practical high frequency brain–computer interface based on steady-state visual evoked potentials using a single channel of EEG

Cited by 43 publications

References 23 publications

Induced Gamma-Band Activity during Actual and Imaginary Movements: EEG Analysis

Induced Gamma-Band Activity during Actual and Imaginary Movements: EEG Analysis

Highly Interactive Brain–Computer Interface Based on Flicker-Free Steady-State Motion Visual Evoked Potential

Use of Sine Shaped High-Frequency Rhythmic Visual Stimuli Patterns for SSVEP Response Analysis and Fatigue Rate Evaluation in Normal Subjects

Contact Info

Product

Resources

About