Special Issue "Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications"

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Theoretical and Computational Neuroscience".

Deadline for manuscript submissions: 24 March 2020.

Special Issue Editor

Dr. Claudio Lucchiari
E-Mail Website
Guest Editor
Department of Philosophy, University of Milan, Milan, Italy
Interests: cognitive science; neuropsychology; neuro-oncology; brain computer interface; EEG; emotions; decision making; decision support systems; creativity

Special Issue Information

Dear Colleagues,

Brain–computer interfaces (BCI) are technological systems that enable individuals to interact with a computer using their brain activity. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action on the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game).

Applications are now available in various research and clinical fields, such as for communication and control tools for severely disabled users, rehabilitation, and human–computer interaction. These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the user’s emotional state by training a classification algorithm to recognize affect states. This information can be utilized to analyze the cortical activity in specific areas during complex tasks, as well as to allow the BCI to adapt its recognition algorithms. Furthermore, information about emotional involvement can be used to improve the user’s experience while controlling the BCI through affective modulation. 

This Special Issue is dedicated to the study of brain activity related to emotional involvement as measured by BCI systems designed for different purposes. Papers reporting theoretical models, targeted reviews, research, and clinical applications are welcome.

Dr. Claudio Lucchiari
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • brain computer interface
  • emotional involvement
  • brain activity
  • human–machine interaction
  • emotion detection methods
  • artificial intelligence
  • rehabilitation
  • emotional BCI control
  • psychological states

Published Papers (2 papers)

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Research

Open AccessArticle
Peak Detection with Online Electroencephalography (EEG) Artifact Removal for Brain–Computer Interface (BCI) Purposes
Brain Sci. 2019, 9(12), 347; https://doi.org/10.3390/brainsci9120347 - 29 Nov 2019
Abstract
Brain–computer interfaces (BCIs) measure brain activity and translate it to control computer programs or external devices. However, the activity generated by the BCI makes measurements for objective fatigue evaluation very difficult, and the situation is further complicated due to different movement artefacts. The [...] Read more.
Brain–computer interfaces (BCIs) measure brain activity and translate it to control computer programs or external devices. However, the activity generated by the BCI makes measurements for objective fatigue evaluation very difficult, and the situation is further complicated due to different movement artefacts. The BCI performance could be increased if an online method existed to measure the fatigue objectively and accurately. While BCI-users are moving, a novel automatic online artefact removal technique is used to filter out these movement artefacts. The effects of this filter on BCI performance and mainly on peak frequency detection during BCI use were investigated in this paper. A successful peak alpha frequency measurement can lead to more accurately determining objective user fatigue. Fifteen subjects performed various imaginary and actual movements in separate tasks, while fourteen electroencephalography (EEG) electrodes were used. Afterwards, a steady-state visual evoked potential (SSVEP)-based BCI speller was used, and the users were instructed to perform various movements. An offline curve fitting method was used for alpha peak detection to assess the effect of the artefact filtering. Peak detection was improved by the filter, by finding 10.91% and 9.68% more alpha peaks during simple EEG recordings and BCI use, respectively. As expected, BCI performance deteriorated from movements, and also from artefact removal. Average information transfer rates (ITRs) were 20.27 bit/min, 16.96 bit/min, and 14.14 bit/min for the (1) movement-free, (2) the moving and unfiltered, and (3) the moving and filtered scenarios, respectively. Full article
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Open AccessArticle
Exploring Shopper’s Browsing Behavior and Attention Level with an EEG Biosensor Cap
Brain Sci. 2019, 9(11), 301; https://doi.org/10.3390/brainsci9110301 - 31 Oct 2019
Abstract
The online shopping market is developing rapidly, meaning that it is important for retailers and manufacturers to understand how consumers behave online compared to when in brick-and-mortar stores. Retailers want consumers to spend time shopping, browsing, and searching for products in the hope [...] Read more.
The online shopping market is developing rapidly, meaning that it is important for retailers and manufacturers to understand how consumers behave online compared to when in brick-and-mortar stores. Retailers want consumers to spend time shopping, browsing, and searching for products in the hope a purchase is made. On the other hand, consumers may want to restrict their duration of stay on websites due to perceived risk of loss of time or convenience. This phenomenon underlies the need to reduce the duration of consumer stay (namely, time pressure) on websites. In this paper, the browsing behavior and attention span of shoppers engaging in online shopping under time pressure were investigated. The attention and meditation level are measured by an electroencephalogram (EEG) biosensor cap. The results indicated that when under time pressure shoppers engaging in online shopping are less attentive. Thus, marketers may need to find strategies to increase a shopper’s attention. Shoppers unfamiliar with product catalogs on shopping websites are less attentive, therefore marketers should adopt an interesting style for product catalogs to hold a shopper’s attention. We discuss our findings and outline their business implications. Full article
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