A periodogram-based method for the detection of steady-state visually evoked potentials

Liavas, Athanasios P.; Moustakides, George V.; Henning, G.; Psarakis, Emmanouil Ζ.; Husar, Peter

doi:10.1109/10.661272

Cited by 49 publications

(31 citation statements)

References 3 publications

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“…Our results generally agree that both f1 and f2 responses are identified in response to periodic stimulation with differential amplitude responses that vary with frequency. Our results do not agree with the findings of Liavas et al (1998), who found a distinct separation in the harmonics identified in response to foveal stimulation (f1, f2, f3 and f4) compared to peripheral stimulation (only f2). Overall, it appears that the visual system is sensitive to the frequency components associated with oscillating stimuli.…”

Section: Dorsal Stream Hypothesiscontrasting

confidence: 82%

Frequency-Following and Connectivity of Different Visual Areas in Response to Contrast-Reversal Stimulation

2006

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The sensitivity of visual areas to different temporal frequencies, as well as the functional connections between these areas, was examined using magnetoencephalography (MEG). Alternating circular sinusoids (0, 3.1, 8.7 and 14 Hz) were presented to foveal and peripheral locations in the visual field to target ventral and dorsal stream structures, respectively. It was hypothesized that higher temporal frequencies would preferentially activate dorsal stream structures. To determine the effect of frequency on the cortical response we analyzed the late time interval (220-770 ms) using a multi-dipole spatio-temporal analysis approach to provide source locations and timecourses for each condition. As an exploratory aspect, we performed cross-correlation analysis on the source timecourses to determine which sources responded similarly within conditions. Contrary to predictions, dorsal stream areas were not activated more frequently during high temporal frequency stimulation. However, across cortical sources the frequency-following response showed a difference, with significantly higher power at the second harmonic for the 3.1 and 8.7 Hz stimulation and at the first and second harmonics for the 14 Hz stimulation with this pattern seen robustly in area V1. Cross-correlations of the source timecourses showed that both low- and high-order visual areas, including dorsal and ventral stream areas, were significantly correlated in the late time interval. The results imply that frequency information is transferred to higher-order visual areas without translation. Despite the less complex waveforms seen in the late interval of time, the cross-correlation results show that visual, temporal and parietal cortical areas are intricately involved in late-interval visual processing.

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Section: Dorsal Stream Hypothesiscontrasting

confidence: 82%

Frequency-Following and Connectivity of Different Visual Areas in Response to Contrast-Reversal Stimulation

2006

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“…• Statistical assessment (''objective response detection'') is relatively easy if performed in the frequency domain (e.g., [1][2][3][4][5]). • In some cases (e.g., flicker electroretinogram), the high stimulation rate is chosen for physiological reasons.…”

Section: Introductionmentioning

confidence: 99%

Some thoughts on the interpretation of steady-state evoked potentials

Heinrich¹

2010

Doc Ophthalmol

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Steady-state evoked potentials are popular due to their easy analysis in frequency space and the availability of methods for objective response detection. However, the interpretation of steady-state responses can be challenging due to their origin as a sequence of responses to single stimuli. In the present paper, issues of signal extinction and some characteristics of higher harmonics are illustrated based on simple model data for those readers who do not regularly hobnob with frequency-space representations of data. It is important to realize that the absence of a steady-state response does not prove the lack of neural activity. For the same underlying reasons, namely constructive and destructive superposition of individual responses, comparisons of amplitudes between experimental conditions are prone to inaccuracies. Thus, before inferring physiology from steady-state responses, one should consider an alternative explanation in terms of signal composition.

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“…The most widely used method to detect SSVEP responses is to apply a Fast Fourier Transform of the EEG signals and to find the corresponding peaks in the ensuing spectra [16]. However, this direct method is sensitive to noise and interference signals when the EEG signals are processed independently.…”

Section: A Steady-state Visual Evoked Potentialsmentioning

confidence: 99%

Brain-Computer Interface for high-level control of rehabilitation robotic systems

Valbuena

Cyriacks

Friman

et al. 2007

2007 IEEE 10th International Conference on Rehabilitation Robotics

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Abstract-In this work, a Brain-Computer Interface (BCI) based on Steady-State Visual Evoked Potentials (SSVEP) is presented as an input device for the human machine interface (HMI) of the semi-autonomous robot FRIEND II. The role of the BCI is to translate high-level requests from the user into control commands for the FRIEND II system. In the current application, the BCI is used to navigate a menu system and to select commands such as pouring a beverage into a glass. The low-level control of the test platform, the rehabilitation robot FRIEND II, is executed by the control architecture MASSiVE, which in turn is served by a planning instance, an environment model and a set of sensors (e.g., machine vision) and actors. The BCI is introduced as a step towards the goal of providing disabled users with at least 1.5 hours independence from care givers.

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A periodogram-based method for the detection of steady-state visually evoked potentials

Cited by 49 publications

References 3 publications

Frequency-Following and Connectivity of Different Visual Areas in Response to Contrast-Reversal Stimulation

Frequency-Following and Connectivity of Different Visual Areas in Response to Contrast-Reversal Stimulation

Some thoughts on the interpretation of steady-state evoked potentials

Brain-Computer Interface for high-level control of rehabilitation robotic systems

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