TY - GEN
T1 - Multi-Objective Optimisation for SSVEP Detection
AU - Zhang, Yue
AU - Zhang, Zhiqiang
AU - Xie, Shengquan
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/7/27
Y1 - 2021/7/27
N2 - Data-driven spatial filtering approaches have been widely used for steady-state visual evoked potentials (SSVEPs) detection toward the brain-computer interface (BCI). The existing methods tend to learn the spatial filter parameters for a certain stimulation frequency only using the training trials from the same stimulus, which may ignore the information from the other stimuli. In this paper, we propose a novel multi-objective optimisation-based spatial filtering method for enhancing SSVEP recognition. Spatial filters are defined via maximising the correlation among the training data from the same stimulus whilst minimising the correlation from different stimuli. We collected SSVEP signals using 16 electrodes from six healthy subjects at 4 different stimulation frequencies: 14Hz, 15Hz, 16Hz, and 17Hz. The experimental study was implemented, and our method can achieve an average recognition accuracy of 94.17%, which illustrates its effectiveness.
AB - Data-driven spatial filtering approaches have been widely used for steady-state visual evoked potentials (SSVEPs) detection toward the brain-computer interface (BCI). The existing methods tend to learn the spatial filter parameters for a certain stimulation frequency only using the training trials from the same stimulus, which may ignore the information from the other stimuli. In this paper, we propose a novel multi-objective optimisation-based spatial filtering method for enhancing SSVEP recognition. Spatial filters are defined via maximising the correlation among the training data from the same stimulus whilst minimising the correlation from different stimuli. We collected SSVEP signals using 16 electrodes from six healthy subjects at 4 different stimulation frequencies: 14Hz, 15Hz, 16Hz, and 17Hz. The experimental study was implemented, and our method can achieve an average recognition accuracy of 94.17%, which illustrates its effectiveness.
KW - Brain-computer interface (BCI)
KW - electroencephalography (EEG)
KW - multi-objective optimisation
KW - steady-state visual evoked potential (SSVEP)
UR - https://www.scopus.com/pages/publications/85114123593
U2 - 10.1109/BSN51625.2021.9507041
DO - 10.1109/BSN51625.2021.9507041
M3 - 会议稿件
AN - SCOPUS:85114123593
T3 - 2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2021
BT - 2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, BSN 2021
Y2 - 27 July 2021 through 30 July 2021
ER -