Dataset related to article "Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction"

<strong>SCORING SEGMENTATIONS</strong> Qualitative segmentation scores by two raters (rater1; rater2). Scale: excellent (4); good (3); doubtful (2) and failed (1). <strong>DATABASES</strong> IXI database, Imperial College of London (https://brain-development.org/ixi-dataset/) Autism Brain Imaging Data Exchange (ABIDE) database (http://fcon_1000.projects.nitrc.org) SchizConnect database (http://schizconnect.org) <strong>SEGMENTATION METHODS</strong> MR-TIM (Taberna et al., 2021), green rows WTS (Liu et al., 2017), red rows <strong>TABLES</strong> <strong>IXI_young </strong> 20 MRI from the IXI database, participants 20–35 years old; MR scanners: Philips Intera 3.0T (HH); Philips Gyroscan Intera 1.5T (G) <strong>IXI_older</strong> 20 MRI from the IXI database, participants 60–75 years old; MR scanners: Philips Intera 3.0T (HH); Philips Gyroscan Intera 1.5T (G) <strong>ABIDE</strong> 10 MRI from the ABIDE database, participants 18-25 years old; MR scanner: Philips Achieva 3.0T <strong>SchizConnect</strong> 10 MRI from the SchizConnect database, participants 19-66 years old; MR scanner: Siemens Trio Tim 3.0T <strong>REFERENCES</strong> Liu, Q., Farahibozorg, S., Porcaro, C., Wenderoth, N., &amp; Mantini, D. (2017). Detecting large-scale networks in the human brain using high-density electroencephalography. Hum Brain Mapp, 38(9), 4631-4643. doi:10.1002/hbm.23688 Taberna, G. A., Samogin, J., &amp; Mantini, D. (2021). Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction. Neuroinformatics. doi:10.1007/s12021-020-09504-5