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Several technologies have been proposed to prevent accidents, being brain\u2013computer interfaces (BCIs) one of the most promising. In this context, BCIs have been used to detect emotional states, concentration issues, or stressful situations, which could play a fundamental role in the road since they are directly related to the drivers\u2019 decisions. However, there is no extensive literature applying BCIs to detect subjects\u2019 emotions in driving scenarios. In such a context, there are some challenges to be solved, such as (i) the impact of performing a driving task on the emotion detection and (ii) which emotions are more detectable in driving scenarios. To improve these challenges, this work proposes a framework focused on detecting emotions using electroencephalography with machine learning and deep learning algorithms. In addition, a use case has been designed where two scenarios are presented. The first scenario consists in listening to sounds as the primary task to perform, while in the second scenario listening to sound becomes a secondary task, being the primary task using a driving simulator. In this way, it is intended to demonstrate whether BCIs are useful in this driving scenario. The results improve those existing in the literature, achieving 99% accuracy for the detection of two emotions (non-stimuli and angry), 93% for three emotions (non-stimuli, angry and neutral) and 75% for four emotions (non-stimuli, angry, neutral and joy).<\/jats:p>","DOI":"10.1007\/s00521-023-08343-0","type":"journal-article","created":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T10:02:31Z","timestamp":1677146551000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Analyzing the impact of Driving tasks when detecting emotions through brain\u2013computer interfaces"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3513-3749","authenticated-orcid":false,"given":"Mario","family":"Quiles P\u00e9rez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enrique Tom\u00e1s","family":"Mart\u00ednez Beltr\u00e1n","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sergio","family":"L\u00f3pez Bernal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gregorio","family":"Mart\u00ednez P\u00e9rez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alberto","family":"Huertas Celdr\u00e1n","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"key":"8343_CR1","unstructured":"World Health Organization: Road traffic injuries (2021). https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/road-traffic-injuries. 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