Abstract
Electroencephalogram (EEG) modeling in brain-computer interface (BCI) provides a theoretical foundation for its development. However, limited by the lack of guidelines in model parameter selection and the inability to obtain personal tissue information in practice, EEG modeling in BCI is mainly focused on the theoretical qualitative level which shows a gap between the theory and its application. Based on such problems, this work combined the surface EEG simulation with a converter based on the generative adversarial network (GAN), to establish the connection from simulated EEG to its application in BCI classification. For the scalp EEGs modeling, a mathematical model was built according to the physics of surface EEG, which consisted of the parallel 3-population neural mass model, the equivalent dipole, and the forward computation. For application, a converter based on the conditional GAN was designed, to transfer the simulated theoretical-only EEG to its practical version, in the lack of individual bio-information. To verify the feasibility, based on the latest microexpression-assisted BCI paradigm proposed by our group, the converted simulated EEGs were used in the training of BCI classifiers. The results indicated that, compared with training with insufficient real data, by adding the simulated EEGs, the overall performance showed a significant improvement (P = 0.04 < 0.05), and the test performance can be improved by 2.17% ± 4.23, in which the largest increase was up to 12.60% ± 1.81. Through this work, the link from theoretical EEG simulation to BCI classification has been initially established, providing an enhanced novel solution for the application of EEG modeling in BCI.
Highlights
Non-invasive brain-computer interface (BCI) as a branch in neuroscience, has continuously been a hotspot in recent decades
neural mass model (NMM) was applied in the EEG simulation based on the scene graph steady-state visual evoked potentials BCI paradigm (Li et al, 2018a) and the EEG driven by facial expression (Zhang et al, 2016)
Encouraged by the work of probabilistic forecasting (Koochali et al, 2019), data augmentation (Fan et al, 2020; Luo et al, 2020), and EEG feature generating (Krishna et al, 2021) with generative adversarial networks (GAN), in this work, we proposed an EEG simulation method with conditional GAN combined to compensate for the lack of tissue information, establishing the link between EEG modeling and its application
Summary
Non-invasive brain-computer interface (BCI) as a branch in neuroscience, has continuously been a hotspot in recent decades. The “neural population” had first been put forward in the 1970s, assuming that all nervous processes can be dependent upon the interaction of excitatory and inhibitory cells (Freeman, 1972, 1973, 1975; Wilson and Cowan, 1972, 1973). Later, it was adopted by Lopes da Silva in modeling the generation of rhythmic activity (Lopesdas et al, 1974; Lopes da Silva et al, 1976). NMM was applied in the EEG simulation based on the scene graph steady-state visual evoked potentials BCI paradigm (Li et al, 2018a) and the EEG driven by facial expression (Zhang et al, 2016)
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