Use of a steady-state baseline to address evoked vs. oscillation models of visual evoked potential origin

Xu, Minpeng; Jia, Yihong; Qi, Hongzhi; Hu, Yong; He, Feng; Zhao, Xin; Zhou, Peng; Zhang, Lixin; Wan, Baikun; Gao, Wei; Ming, Dong

doi:10.1016/j.neuroimage.2016.03.073

Cited by 21 publications

(24 citation statements)

References 42 publications

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“…We approach this debate differently, by analyzing the spatiotemporal dynamics of phase resetting and additive amplitudes. Comparing the strength of phase resetting and additive amplitude effects directly is generally not possible without carefully controlled stimuli (Xu et al, 2016) and, indeed, when we used single-electrode measures, we could identify the presence of both mechanisms but could not compare their strength. We then introduced a correlation-based method that allows comparison of the relative impact of evoked and induced effects in any spatially extended recording by incorporating the spatial structure of activity into previous measures (Martínez-Montes et al, 2008).…”

Section: Trial-averaged Spatiotemporal Patternsmentioning

confidence: 99%

Visual Motion Discrimination by Propagating Patterns in Primate Cerebral Cortex

et al. 2017

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Visual stimuli can evoke waves of neural activity that propagate across the surface of visual cortical areas. The relevance of these waves for visual processing is unknown. Here, we measured the phase and amplitude of local field potentials (LFPs) in electrode array recordings from the motion-processing medial temporal (MT) area of anesthetized male marmosets. Animals viewed grating or dot-field stimuli drifting in different directions. We found that, on individual trials, the direction of LFP wave propagation is sensitive to the direction of stimulus motion. Propagating LFP patterns are also detectable in trial-averaged activity, but the trial-averaged patterns exhibit different dynamics and behaviors from those in single trials and are similar across motion directions. We show that this difference arises because stimulus-sensitive propagating patterns are present in the phase of single-trial oscillations, whereas the trial-averaged signal is dominated by additive amplitude effects. Our results demonstrate that propagating LFP patterns can represent sensory inputs at timescales relevant to visually guided behaviors and raise the possibility that propagating activity patterns serve neural information processing in area MT and other cortical areas. Propagating wave patterns are widely observed in the cortex, but their functional relevance remains unknown. We show here that visual stimuli generate propagating wave patterns in local field potentials (LFPs) in a movement-sensitive area of the primate cortex and that the propagation direction of these patterns is sensitive to stimulus motion direction. We also show that averaging LFP signals across multiple stimulus presentations (trial averaging) yields propagating patterns that capture different dynamic properties of the LFP response and show negligible direction sensitivity. Our results demonstrate that sensory stimuli can modulate propagating wave patterns reliably in the cortex. The relevant dynamics are normally masked by trial averaging, which is a conventional step in LFP signal processing.

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Section: Trial-averaged Spatiotemporal Patternsmentioning

confidence: 99%

Visual Motion Discrimination by Propagating Patterns in Primate Cerebral Cortex

et al. 2017

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“…In addition to the column-wise averaging of epochs, the dynamics of oscillatory evoked potentials has been analyzed by filtering the single-trial measurements in a narrow frequency band around the frequency of interest and computing the envelope of the power signal using low-pass filtering 26 . Likewise, single trial analysis has been implemented to characterize the transition period that precedes the stable region of SSVEP 48 , and the changes in amplitude and phase of the SSVEP during the stable region of the response 49 . While single trial analyses allow discrimination of relatively fast fluctuations in response amplitude, experimental designs to analyze the average response in blocks separated with a given inter-block interval only account for long-term variations in the amplitude of the evoked potential 50,51 .…”

Section: Discussionmentioning

confidence: 99%

“…However, evidence indicates that the phase of brain oscillatory responses is less variable than expected. In fact, several studies have reported a regularity in the expected phase of the human 80 Hz ASSR 47,48,49 . When latencies are estimated based on the phase of the oscillatory activity, the predictable effect of the intensity and the carrier frequency of the acoustic stimuli on the latency of the auditory responses has been observed (i.e., the latency decrease as the intensity and carrier frequency increase) 52,53,54 .…”

Section: Discussionmentioning

confidence: 99%

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Prado

Otero

Martı́nez-Montes

et al. 2019

JoVE

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Neural entrainment refers to the synchronization of neural activity to the periodicity of sensory stimuli. This synchronization defines the generation of steady-state evoked responses (i.e., oscillations in the electroencephalogram phase-locked to the driving stimuli). The classic interpretation of the amplitude of the steady-state evoked responses assumes a stereotypical time-invariant neural response plus random background fluctuations, such that averaging over repeated presentations of the stimulus recovers the stereotypical response. This approach ignores the dynamics of the steady-state, as in the case of the adaptation elicited by prolonged exposures to the stimulus. To analyze the dynamics of steady-state responses, it can be assumed that the time evolution of the response amplitude is the same in different stimulation runs separated by sufficiently long breaks. Based on this assumption, a method to characterize the time evolution of steady-state responses is presented. A sufficiently large number of recordings are acquired in response to the same experimental condition. Experimental runs (recordings) are column-wise averaged (i.e., runs are averaged but epoch within recordings are not averaged with the preceding segments). The columnwise averaging allows analysis of steady-state responses in recordings with remarkably high signal-to-noise ratios. Therefore, the averaged signal provides an accurate representation of the time evolution of the steady-state response, which can be analyzed in both the time and frequency domains. In this study, a detailed description of the method is provided, using steady-state visually evoked potentials as an example of a response. Advantages and caveats are evaluated based on a comparison with single-trial methods designed to analyze neural entrainment.

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“…Brain Computer Interface (BCI) is a direct communication system between the brain and external devices, which does not rely on human peripheral nerves and muscles (Wolpaw et al, 2002 ; Nicolas-Alonso and Gomez-Gil, 2012 ). In the past few decades, BCI technology has achieved great progress and found a wide range of application scenarios in daily life, such as wheelchair control for disabled patients, entertainment and smart home control for healthy users (Yin et al, 2013 ; Herweg et al, 2016 ; Xu et al, 2016 ). Most of the BCI systems are EEG-based due to its relatively low expense, high portability, better time resolution, and minimal risks to users as compared to other modalities (Arvaneh et al, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Temporal Combination Pattern Optimization Based on Feature Selection Method for Motor Imagery BCIs

Jiang

Wang

et al. 2020

Front. Hum. Neurosci.

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Common spatial pattern (CSP) method is widely used for spatial filtering and brain pattern extraction from electroencephalogram (EEG) signals in motor imagery (MI)-based brain-computer interfaces (BCIs). The participant-specific time window relative to the visual cue has a significant impact on the effectiveness of the CSP. However, the time window is usually selected experientially or manually. To solve this problem, we propose a novel feature selection approach for MI-based BCIs. Specifically, multiple time segments were obtained by decomposing each EEG sample of the MI task. Furthermore, the features were extracted by CSP from each time segment and were combined to form a new feature vector. Finally, the optimal temporal combination patterns for the new feature vector were selected based on four feature selection algorithms, i.e., mutual information, least absolute shrinkage and selection operator, principal component analysis and stepwise linear discriminant analysis (denoted as MUIN, LASSO, PCA, and SWLDA, respectively), and the classification algorithm was employed to evaluate the average classification accuracy. With three BCI competition datasets, the results of the four proposed algorithms were compared with traditional CSP algorithm in classification accuracy. Experimental results show that compared with traditional algorithm, the proposed methods significantly improve performance. Specifically, the LASSO achieved the highest accuracy (88.58%) among the proposed methods. Importantly, the average classification accuracies using the proposed approaches significantly improved 10.14% (MUIN), 11.40% (LASSO), 6.08% (PCA), and 10.25% (SWLDA) compared to that using CSP. These results indicate that the proposed approach is expected to be practical in MI-based BCIs.

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Use of a steady-state baseline to address evoked vs. oscillation models of visual evoked potential origin

Cited by 21 publications

References 42 publications

Visual Motion Discrimination by Propagating Patterns in Primate Cerebral Cortex

Visual Motion Discrimination by Propagating Patterns in Primate Cerebral Cortex

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Temporal Combination Pattern Optimization Based on Feature Selection Method for Motor Imagery BCIs

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