Please use this identifier to cite or link to this item:
For citation please use:
Main Title: A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity
Author(s): Sannelli, Claudia
Vidaurre, Carmen
Müller, Klaus-Robert
Blankertz, Benjamin
Type: Article
Abstract: Brain-Computer Interfaces (BCIs) are inefficient for a non-negligible part of the population, estimated around 25%. To understand this phenomenon in Sensorimotor Rhythm (SMR) based BCIs, data from a large-scale screening study conducted on 80 novice participants with the Berlin BCI system and its standard machine-learning approach were investigated. Each participant performed one BCI session with resting state Encephalography, Motor Observation, Motor Execution and Motor Imagery recordings and 128 electrodes. A significant portion of the participants (40%) could not achieve BCI control (feedback performance > 70%). Based on the performance of the calibration and feedback runs, BCI users were stratified in three groups. Analyses directed to detect and elucidate the differences in the SMR activity of these groups were performed. Statistics on reactive frequencies, task prevalence and classification results are reported. Based on their SMR activity, also a systematic list of potential reasons leading to performance drops and thus hints for possible improvements of BCI experimental design are given. The categorization of BCI users has several advantages, allowing researchers 1) to select subjects for further analyses as well as for testing new BCI paradigms or algorithms, 2) to adopt a better subject-dependent training strategy and 3) easier comparisons between different studies.
Subject(s): brain-computer interface
sensorimotor rhythm
Issue Date: 25-Jan-2019
Date Available: 11-Dec-2019
Is Supplemented By: 10.14279/depositonce-8102
Language Code: en
DDC Class: 150 Psychologie
610 Medizin und Gesundheit
Sponsor/Funder: BMBF, 01GQ0850, Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktion
Journal Title: PLOS ONE
Publisher: Public Library of Science (PLoS)
Volume: 14
Issue: 1
Article Number: e0207351
Publisher DOI: 10.1371/journal.pone.0207351
EISSN: 1932-6203
TU Affiliation(s): Fak. 4 Elektrotechnik und Informatik » Inst. Softwaretechnik und Theoretische Informatik » FG Neurotechnologie
Appears in Collections:Technische Universität Berlin » Publications

Files in This Item:
Format: Adobe PDF | Size: 5.97 MB
DownloadShow Preview

Item Export Bar

This item is licensed under a Creative Commons License Creative Commons