{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T03:08:47Z","timestamp":1775272127474,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T00:00:00Z","timestamp":1645660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, we designed from scratch, realized, and characterized a six-channel EEG wearable headband for the measurement of stress-related brain activity during driving. The headband transmits data over WiFi to a laptop, and the rechargeable battery life is 10 h of continuous transmission. The characterization manifested a measurement error of 6 \u03bcV in reading EEG channels, and the bandwidth was in the range [0.8, 44] Hz, while the resolution was 50 nV exploiting the oversampling technique. Thanks to the full metrological characterization presented in this paper, we provide important information regarding the accuracy of the sensor because, in the literature, commercial EEG sensors are used even if their accuracy is not provided in the manuals. We set up an experiment using the driving simulator available in our laboratory at the University of Udine; the experiment involved ten volunteers who had to drive in three scenarios: manual, autonomous vehicle with a \u201cgentle\u201d approach, and autonomous vehicle with an \u201caggressive\u201d approach. The aim of the experiment was to assess how autonomous driving algorithms impact EEG brain activity. To our knowledge, this is the first study to compare different autonomous driving algorithms in terms of drivers\u2019 acceptability by means of EEG signals. The obtained results demonstrated that the estimated power of beta waves (related to stress) is higher in the manual with respect to autonomous driving algorithms, either \u201cgentle\u201d or \u201caggressive\u201d.<\/jats:p>","DOI":"10.3390\/s22051785","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T21:11:07Z","timestamp":1645737067000},"page":"1785","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Development of an EEG Headband for Stress Measurement on Driving Simulators"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6963-0196","authenticated-orcid":false,"given":"Antonio","family":"Affanni","sequence":"first","affiliation":[{"name":"Polytechnic Department of Engineering and Architecture, University of Udine, 33100 Udine, UD, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9406-3072","authenticated-orcid":false,"given":"Taraneh","family":"Aminosharieh Najafi","sequence":"additional","affiliation":[{"name":"Polytechnic Department of Engineering and Architecture, University of Udine, 33100 Udine, UD, Italy"}]},{"given":"Sonia","family":"Guerci","sequence":"additional","affiliation":[{"name":"Eurisoft S. 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