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In the previous decade, breakthroughs in the central nervous system bioinformatics and computational innovation have prompted significant developments in brain–computer interface (BCI), elevating it to the forefront of applied science and research. BCI revitalization enables neurorehabilitation strategies for physically disabled patients (e.g., disabled patients and hemiplegia) and patients with brain injury (e.g., patients with stroke). Different methods have been developed for electroencephalogram (EEG)-based BCI applications. Due to the lack of a large set of EEG data, methods using matrix factorization and machine learning were the most popular. However, things have changed recently because a number of large, high-quality EEG datasets are now being made public and used in deep learning-based BCI applications. On the other hand, deep learning is demonstrating great prospects for solving complex relevant tasks such as motor imagery classification, epileptic seizure detection, and driver attention recognition using EEG data. Researchers are doing a lot of work on deep learning-based approaches in the BCI field right now. Moreover, there is a great demand for a study that emphasizes only deep learning models for EEG-based BCI applications. Therefore, we introduce this study to the recent proposed deep learning-based approaches in BCI using EEG data (from 2017 to 2022). The main differences, such as merits, drawbacks, and applications are introduced. Furthermore, we point out current challenges and the directions for future studies. We argue that this review study will help the EEG research community in their future research.

Despite the robust evidence that congruent background music in the physical store environment positively affects consumer reactions, less is known about its effects in an online context. The present study aims (1) to examine whether congruency via multiple elicited crossmodal correspondences between background music and the online store environment (e.g., perceived lightness, loudness, and coldness of the cue/environment) leads to more positive affective, evaluative, and behavioral consumer reactions and (2) to investigate the moderating role of shopping goals on this crossmodal congruency effect. Previous research showed that low task-relevant atmospheric cues like music can have a negative effect on consumers when they visit a website with a purchase goal in mind. An online experiment was conducted with 239 respondents randomly assigned to a shopping goal (experiential browsing vs. goal-directed searching) and a music condition (no music, crossmodally congruent music, or crossmodally incongruent music). Our results show that crossmodally incongruent background music (vs. no music) leads to more positive consumer reactions for experiential browsers and more negative consumer reactions for goal-directed searchers. Conversely, crossmodally congruent background music (vs. no music) has a positive effect on experiential browsers and no adverse effect on goal-directed searchers. Additionally, the presence of crossmodally congruent background music leads to more positive consumer reactions than the presence of crossmodally incongruent background music, independent of the shopping goal. We extend previous research on multisensory congruency effects by showing the added value of establishing congruency between music and the store environment via multiple elicited crossmodal correspondences in the online environment, countering previously found negative effects of low-task relevant atmospheric cues for goal-directed searchers.

We present a perspective article on the state of multisensory human–food interaction (MHFI) research and lay out some reflections for research and development in this area of inquiry, based on a revision of the different spaces that we have co-created with researchers in this space. We begin by conceptualizing and defining MHFI, before moving onto presenting some of its major themes, as well as possible ways in which such themes can guide future research in the area. This article provides key definitions and foundations for the area of MHFI, as well as a first point of contact for those interested in it.