Ga-Young Choi1, Chang-Hee Han2, Young-Jin Jung3, Han-Jeong Hwang1. 1. Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Daehak-ro 61, Gumi 39177, Republic of Korea. 2. Machine Learning Group, Berlin Institute of Technology (TU Berlin), Marchstrasse 23, Berlin 10587, Germany. 3. Department of Radiological Science, Dongseo University, Jurye-ro 47, Busan 47011, Republic of Korea.
Abstract
BACKGROUND: A steady-state visual-evoked potential (SSVEP) is a brain response to visual stimuli modulated at certain frequencies; it has been widely used in electroencephalography (EEG)-based brain-computer interface research. However, there are few published SSVEP datasets for brain-computer interface. In this study, we obtained a new SSVEP dataset based on measurements from 30 participants, performed on 2 days; our dataset complements existing SSVEP datasets: (i) multi-band SSVEP datasets are provided, and all 3 possible frequency bands (low, middle, and high) were used for SSVEP stimulation; (ii) multi-day datasets are included; and (iii) the EEG datasets include simultaneously obtained physiological measurements, such as respiration, electrocardiography, electromyography, and head motion (accelerator). FINDINGS: To validate our dataset, we estimated the spectral powers and classification performance for the EEG (SSVEP) datasets and created an example plot to visualize the physiological time-series data. Strong SSVEP responses were observed at stimulation frequencies, and the mean classification performance of the middle frequency band was significantly higher than the low- and high-frequency bands. Other physiological data also showed reasonable results. CONCLUSIONS: Our multi-band, multi-day SSVEP datasets can be used to optimize stimulation frequencies because they enable simultaneous investigation of the characteristics of the SSVEPs evoked in each of the 3 frequency bands, and solve session-to-session (day-to-day) transfer problems by enabling investigation of the non-stationarity of SSVEPs measured on different days. Additionally, auxiliary physiological data can be used to explore the relationship between SSVEP characteristics and physiological conditions, providing useful information for optimizing experimental paradigms to achieve high performance.
BACKGROUND: A steady-state visual-evoked potential (SSVEP) is a brain response to visual stimuli modulated at certain frequencies; it has been widely used in electroencephalography (EEG)-based brain-computer interface research. However, there are few published SSVEP datasets for brain-computer interface. In this study, we obtained a new SSVEP dataset based on measurements from 30 participants, performed on 2 days; our dataset complements existing SSVEP datasets: (i) multi-band SSVEP datasets are provided, and all 3 possible frequency bands (low, middle, and high) were used for SSVEP stimulation; (ii) multi-day datasets are included; and (iii) the EEG datasets include simultaneously obtained physiological measurements, such as respiration, electrocardiography, electromyography, and head motion (accelerator). FINDINGS: To validate our dataset, we estimated the spectral powers and classification performance for the EEG (SSVEP) datasets and created an example plot to visualize the physiological time-series data. Strong SSVEP responses were observed at stimulation frequencies, and the mean classification performance of the middle frequency band was significantly higher than the low- and high-frequency bands. Other physiological data also showed reasonable results. CONCLUSIONS: Our multi-band, multi-day SSVEP datasets can be used to optimize stimulation frequencies because they enable simultaneous investigation of the characteristics of the SSVEPs evoked in each of the 3 frequency bands, and solve session-to-session (day-to-day) transfer problems by enabling investigation of the non-stationarity of SSVEPs measured on different days. Additionally, auxiliary physiological data can be used to explore the relationship between SSVEP characteristics and physiological conditions, providing useful information for optimizing experimental paradigms to achieve high performance.
Authors: Henrik Gollee; Ivan Volosyak; Angus J McLachlan; Kenneth J Hunt; Axel Gräser Journal: IEEE Trans Biomed Eng Date: 2010-02-18 Impact factor: 4.538
Authors: Amer I Aladin; Seamus P Whelton; Mouaz H Al-Mallah; Michael J Blaha; Steven J Keteyian; Stephen P Juraschek; Jonathan Rubin; Clinton A Brawner; Erin D Michos Journal: Am J Cardiol Date: 2014-09-16 Impact factor: 2.778