Adolescent to young adult longitudinal development across 8 years for matching emotional stimuli during functional magnetic resonance imaging

Vetter, Nora C.; Fröhner, Juliane H.; Hoffmann, Klara; Backhausen, Lea L.; Smolka, Michael N.

doi:10.1016/j.dcn.2022.101131

Cited by 1 publication

(2 citation statements)

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“…In this study, we analyzed the signals of young adults in their early twenties. However, the brain activity of young adults and older groups differs [76,77]. Furthermore, it is known that some emotional attention circuits continue to mature when people are in their twenties [76,78].…”

Section: Limitationsmentioning

confidence: 99%

“…However, the brain activity of young adults and older groups differs [76,77]. Furthermore, it is known that some emotional attention circuits continue to mature when people are in their twenties [76,78]. Therefore, it is possible that brain signals' characteristics appear differently depending upon age, and studies have reported that there is a difference in SSVEP BCI performance between young adults and older groups [79,80].…”

Section: Limitationsmentioning

confidence: 99%

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Exploring the Ability to Classify Visual Perception and Visual Imagery EEG Data: Toward an Intuitive BCI System

2022

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Providing an intuitive interface for the actual use of brain–computer interface (BCI) can increase BCI users’ convenience greatly. We explored the possibility that visual imagery can be used as a paradigm that may constitute a more intuitive, active BCI. To do so, electroencephalography (EEG) data were collected during visual perception and imagery experiments. Three image categories (object, digit, shape) and three different images per category were used as visual stimuli. EEG data from seven subjects were used in this work. Three types of visual perception/imagery EEG data were preprocessed for classification: raw time series data; time–frequency maps; and common spatial pattern (CSP). Five types of classifiers (EEGNet, 1D convolutional neural network (CNN), MultiRocket, MobileNet, support vector machine (SVM)) were applied to each applicable data type among the three preprocessed types. Thus, we investigated the feasibility of classifying three-category or nine-class visual perception/imagery over various classifiers and preprocessed data types. We found that the MultiRocket network showed the best classification performance: yielding approximately 57.02% (max 63.62%) for three-category classification in visual perception and approximately 46.43% (max 71.38%) accuracy for three-category classification in visual imagery. However, no meaningfully improved performance was achieved in the nine-class classification in either visual perception or imagery, although visual perception yielded slightly higher accuracy than visual imagery. From our extensive investigation, we found that visual perception and visual imagery data may be classified; however, it is somewhat doubtful whether either may be applicable to an actual BCI system. It is believed that introducing better-designed advanced deep learning networks together with more informative feature extractions may improve the performance of EEG visual perception/imagery classifications. In addition, a more sophisticated experimental design paradigm may enhance the potential to achieve more intuitive visual imagery BCI.

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