Lambda response as an index of visual perception research

Yagi, Akihiro

doi:10.4992/psycholres1954.24.106

Cited by 13 publications

(3 citation statements)

References 11 publications

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“…The EMRP waveforms in the present study featured a prominent lambda potential complex ( Yagi, 1979 , 1981b ). The P1 component of lambda potential in the present study had a slightly longer peak latency of 120 ms compared to the 70–80 ms latency seen in earlier studies ( Yagi, 1982 ; Thickbroom et al, 1991 ), but was close to the peak amplitude at a latency of 100 ms reported in more recent studies ( Dimigen et al, 2011 ; Körner et al, 2014 ). These latency differences may be related to methodological and experimental variations across studies ( Kamienkowski et al, 2012 ).…”

Section: Discussionsupporting

confidence: 81%

Brain Responses to Emotional Faces in Natural Settings: A Wireless Mobile EEG Recording Study

et al. 2018

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The detection of a human face in a visual field and correct reading of emotional expression of faces are important elements in everyday social interactions, decision making and emotional responses. Although brain correlates of face processing have been established in previous fMRI and electroencephalography (EEG)/MEG studies, little is known about how the brain representation of faces and emotional expressions of faces in freely moving humans. The present study aimed to detect brain electrical potentials that occur during the viewing of human faces in natural settings. 64-channel wireless EEG and eye-tracking data were recorded in 19 participants while they moved in a mock art gallery and stopped at times to evaluate pictures hung on the walls. Positive, negative and neutral valence pictures of objects and human faces were displayed. The time instants in which pictures first occurred in the visual field were identified in eye-tracking data and used to reconstruct the triggers in continuous EEG data after synchronizing the time axes of the EEG and eye-tracking device. EEG data showed a clear face-related event-related potential (ERP) in the latency interval ranging from 165 to 210 ms (N170); this component was not seen whilst participants were viewing non-living objects. The face ERP component was stronger during viewing disgusted compared to neutral faces. Source dipole analysis revealed an equivalent current dipole in the right fusiform gyrus (BA37) accounting for N170 potential. Our study demonstrates for the first time the possibility of recording brain responses to human faces and emotional expressions in natural settings. This finding opens new possibilities for clinical, developmental, social, forensic, or marketing research in which information about face processing is of importance.

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Section: Discussionsupporting

confidence: 81%

Brain Responses to Emotional Faces in Natural Settings: A Wireless Mobile EEG Recording Study

et al. 2018

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“…A higher level of attention may be assumed at the beginning of a task. This could be one interpretation of the greater amplitude of the lambda wave elicited at the first fixation onset, following the same speculations in ( 42 ); ( 43 ).…”

Section: Discussionsupporting

confidence: 63%

Estimation of overlapped Eye Fixation Related Potentials: The General Linear Model, a more flexible framework than the ADJAR algorithm

Kristensen¹,

Rivet²,

Guérin–Dugué³

2017

JEMR

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The Eye Fixation Related Potential (EFRP) estimation is the average of EEG signals across epochs at ocular fixation onset. Its main limitation is the overlapping issue. Inter Fixation Intervals (IFI) - typically around 300 ms in the case of unrestricted eye movement- depend on participants’ oculomotor patterns, and can be shorter than the latency of the components of the evoked potential. If the duration of an epoch is longer than the IFI value, more than one fixation can occur, and some overlapping between adjacent neural responses ensues. The classical average does not take into account either the presence of several fixations during an epoch or overlapping. The Adjacent Response algorithm (ADJAR), which is popular for event-related potential estimation, was compared to the General Linear Model (GLM) on a real dataset from a conjoint EEG and eye-tracking experiment to address the overlapping issue. The results showed that the ADJAR algorithm was based on assumptions that were too restrictive for EFRP estimation. The General Linear Model appeared to be more robust and efficient. Different configurations of this model were compared to estimate the potential elicited at image onset, as well as EFRP at the beginning of exploration. These configurations took into account the overlap between the event-related potential at stimulus presentation and the following EFRP, and the distinction between the potential elicited by the first fixation onset and subsequent ones. The choice of the General Linear Model configuration was a tradeoff between assumptions about expected behavior and the quality of the EFRP estimation: the number of different potentials estimated by a given model must be controlled to avoid erroneous estimations with large variances.

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“…With averaged EEGs time‐locked to saccades during wakefulness, positive cerebral potentials, called a lambda responses, were observed at occipital sites 35–40 . The lambda response is not observed when participants are in a dark room or when they close their eyes 35–38 .…”

Section: Dream Images Are Generated At the Offset Of Rapid Eye Movemementioning

confidence: 99%

Brain potentials related to rapid eye movements and dreaming during REM sleep: A short review of psychophysiological correlates

Hori

Ogawa

Abe³

et al. 2008

Sleep and Biological Rhythms

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Many sleep researchers have examined dream image-generation, which occurs during rapid eye movement (REM) sleep. Furthermore, activation of brain regions related to rapid eye movements during human REM sleep has been reported. Although recent brain imaging techniques have high spatial resolution, their temporal resolution is limited. Consequently, the spatio-temporal structures of brain activities related to rapid eye movement remain largely unknown. This article presents a short review of findings of recent studies that have used brain potentials related to rapid eye movement to examine dream image-generation processes. Brain potentials related to rapid eye movement are obtained using averaged electroencephalography (EEG) that is time-locked to the onset or offset of rapid eye movement. First, based on findings related to presaccadic and pre-REM brain potentials, we discuss why eyes move during REM sleep. Second, the relationship of lambdalike potentials, which occur immediately after the cessation of rapid eye movement, to generation of visual dream images, is discussed. Third, enhancement of gamma-band EEG activity occurring immediately after the offset of rapid eye movement is discussed in terms of information-binding of dream images. Finally in this review, preparatory activation of emotion and memory circuits before the onset of rapid eye movement is discussed in terms of contextual setting of the dream story.

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Lambda response as an index of visual perception research

Cited by 13 publications

References 11 publications

Brain Responses to Emotional Faces in Natural Settings: A Wireless Mobile EEG Recording Study

Brain Responses to Emotional Faces in Natural Settings: A Wireless Mobile EEG Recording Study

Estimation of overlapped Eye Fixation Related Potentials: The General Linear Model, a more flexible framework than the ADJAR algorithm

Brain potentials related to rapid eye movements and dreaming during REM sleep: A short review of psychophysiological correlates

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