Auditory cues facilitate object movement processing in human extrastriate visual cortex during simulated self-motion: A pilot study

Vaina, Lucia M.; Calabro, Finnegan J.; Samal, Abhisek; Rana, Kunjan D.; Mamashli, Fahimeh; Khan, Sheraz; Hämäläinen, Matti S.; Ahlfors, Seppo P.; Ahveninen, Jyrki

doi:10.1016/j.brainres.2021.147489

Cited by 3 publications

(1 citation statement)

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“…Vaina, L. M. et al proposed an improved method that combines the spatial depth information and color brightness information of images. This method can effectively detect the motion of multiple targets, but when different targets are at the same pixel depth, although there is different color information, it is difficult to detect moving targets [4]. Liang, S. et al used EM learning to update the Gaussian model, and through the introduction and analysis of the algorithm, the EM algorithm is an effective algorithm for parameter estimation, but it requires pre allocation of a large amount of storage space and has poor real-time performance.…”

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Research on a Human Moving Object Detection Method Based on Gaussian Model and Deep Learning

Sun

2024

SCPE

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In order to understand human motion object detection methods, the author proposes a research on human motion object detection method based on Gaussian model. Firstly, traditional Gaussian models are unable to detect complex scenes or slow moving targets. Therefore, an improved Gaussian model based moving object detection algorithm is proposed. Secondly, multiple Gaussian models are used to represent the features of each pixel in the moving target image, and based on the matching of each pixel in the image with the Gaussian model, it is considered as a background point. Conversely, it is based on the principle of the foreground, and the Gaussian model is updated. Finally, by updating the foreground model and calculating short-term stability indicators, the detection effect of moving targets is improved. By determining the Gaussian distribution and pixel relationship, new parameters are set to construct the background model and eliminate the impact caused by sudden changes in lighting. The experimental analysis results show that this method can effectively detect and track moving targets, with good noise resistance, high clarity, and an accuracy rate of up to 99%. Compared with traditional Gaussian model methods, the improved method can more effectively detect moving targets and has better robustness.

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Research on a Human Moving Object Detection Method Based on Gaussian Model and Deep Learning

Sun

2024

SCPE

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fNIRS Changes in the Middle Temporal and Occipital Cortices After a Cochlear Implant

Huang,

Sun

et al. 2024

The Laryngoscope

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ObjectivesThe relationship between the middle temporal (MTG) and occipital cortices in post‐lingually deaf (PLD) individuals is unclear. This study aimed to investigate changes in the MTG and occipital cortices excitability and their effects on the occipital cortex in individuals with PLD after receiving a cochlear implant (CI).MethodsTwenty‐six individuals with severe‐to‐profound binaural sensorineural PLD were assessed clinically. Nine individuals had received a unilateral cochlear implant over 6 months, while 17 had not. Brodmann area 19 (BA19, extra‐striate occipital cortex) and MTG (auditory‐related area of cortex) were selected as regions of interest. The excitability of the ROI was observed and compared in the surgery and no‐surgery groups by functional near‐infrared spectroscopy (fNIRS) in the resting state, and correlations between connectivity of the MTG and occipital cortex, and as well as the duration of time that had elapsed following CI surgery, were investigated.ResultsfNIRS revealed enhanced global cortical connectivity in the BA19 and MTG on the operative side (p < 0.05) and the connectivity between BA19 and the MTG also increased (p < 0.05). The connectivity between the MTG and BA19 was positively correlated with the duration of cochlear implantation, as was the case for BA18.ConclusionThere was evidence for remodeling of the cerebral cortex: increased excitability was observed in the MTG and BA19, and their connectivity was enhanced, indicating a synergistic effect. Moreover, the MTG may further stimulate the visual cortex by strengthening their connectivity after CI.Level of Evidence3 Laryngoscope, 2024

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Multimodal Sensory Computing

Liao

Chai

2022

Near-Sensor and in-Sensor Computing

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Auditory cues facilitate object movement processing in human extrastriate visual cortex during simulated self-motion: A pilot study

Cited by 3 publications

References 77 publications

Research on a Human Moving Object Detection Method Based on Gaussian Model and Deep Learning

Research on a Human Moving Object Detection Method Based on Gaussian Model and Deep Learning

fNIRS Changes in the Middle Temporal and Occipital Cortices After a Cochlear Implant

Multimodal Sensory Computing

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