TY - GEN
T1 - Spatio-Temporal Filtering for Evoked Potentials Detection
AU - Piela, Michał
AU - Kotas, Marian
AU - Ortiz, Sonia Helena Contreras
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - We propose the new application of the spatio-temporal filtering (STF) method, which is a detection of visual evoked potentials applied to brain-computer interfaces (BCI). STF aims in creating a new, enhanced channel basing on the current and the neighbouring samples from all the input channels. The new channel of the better quality facilitates quick detection of visual evoked potential in the EEG recording by reducing number of averaging operations. The BCI experiments include precise information on the times the specific events took place. This feature allowed us to design very accurately the learning step which is based on generalized eigendecomposition and aims in determining the spatio-temporal filter weights. STF based algorithm allows to achieve good results for enhancement and detection of visual evoked potentials applied for brain-computer interfaces. Advantageous classification accuracies obtained with the use of combined spatial and temporal approach suggest the method can contribute to improvement of the existing solutions and stimulate development of more accurate and faster EEG based interfaces between machines and humans.
AB - We propose the new application of the spatio-temporal filtering (STF) method, which is a detection of visual evoked potentials applied to brain-computer interfaces (BCI). STF aims in creating a new, enhanced channel basing on the current and the neighbouring samples from all the input channels. The new channel of the better quality facilitates quick detection of visual evoked potential in the EEG recording by reducing number of averaging operations. The BCI experiments include precise information on the times the specific events took place. This feature allowed us to design very accurately the learning step which is based on generalized eigendecomposition and aims in determining the spatio-temporal filter weights. STF based algorithm allows to achieve good results for enhancement and detection of visual evoked potentials applied for brain-computer interfaces. Advantageous classification accuracies obtained with the use of combined spatial and temporal approach suggest the method can contribute to improvement of the existing solutions and stimulate development of more accurate and faster EEG based interfaces between machines and humans.
KW - Brain-computer interfaces
KW - Spatio-temporal filtering
KW - Visual evoked potentials
UR - https://www.scopus.com/pages/publications/85075905215
U2 - 10.1007/978-3-030-31964-9_4
DO - 10.1007/978-3-030-31964-9_4
M3 - Conference contribution
AN - SCOPUS:85075905215
SN - 9783030319632
T3 - Advances in Intelligent Systems and Computing
SP - 34
EP - 43
BT - Man-Machine Interactions 6 - 6th International Conference on Man-Machine Interactions, ICMMI 2019
A2 - Gruca, Aleksandra
A2 - Deorowicz, Sebastian
A2 - Harezlak, Katarzyna
A2 - Piotrowska, Agnieszka
A2 - Czachórski, Tadeusz
PB - Springer
T2 - 6th International Conference on Man-Machine Interactions, ICMMI 2019
Y2 - 2 October 2019 through 3 October 2019
ER -