Deep Neural Networks have been applied very successfully in image recognition and natural language processing . Recently these powerful methods have received attention also in the brain-computer interface (BCI) community. Here, we introduce a convolutional neural network (CNN ) architecture optimized for classification of brain states from non-invasive magnetoencephalographic (MEG) measurements. The model structure is motivated by a state-of-the-art generative model of the MEG signal and is thus readily interpretable in neurophysiological terms. We demonstrate that the proposed model is highly accurate in decoding event-related responses as well as modulations of oscillatory brain activity , and is robust with respect to inter-individual differences . Importantly, the model generalizes well across users: when trained on data pooled from previous users, it can successfully perform on new users. Thus, the time-consuming BCI calibration can be omitted. Moreover, the model can be incrementally updated, resulting in +8.9\% average accuracy improvement in offline experiments and +17.0\% in a real-time BCI. We argue that this model can be used in practical BCIs and basic neuroscience research .
Convolutional Neural Networks (CNN) outperform traditional classification methods in many domains . Recently these methods have gained attention in neuroscience and particularly in brain-computer interface (BCI) community. Here, we introduce a CNN optimized for classification of brain states from magnetoencephalographic (MEG) measurements. Our CNN design is based on a generative model of the electromagnetic (EEG and MEG) brain signals and is readily interpretable in neurophysiological terms. We show here that the proposed network is able to decode event-related responses as well as modulations of oscillatory brain activity and that it outperforms more complex neural networks and traditional classifiers used in the field . Importantly, the model is robust to inter-individual differences and can successfully generalize to new subjects in offline and online classification .
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Deep Neural Networks have been applied very successfully in image recognition and natural language processing
Convolutional Neural Networks (CNN) outperform traditional classification methods in many domains
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powerful methods have received attention also in the
methods have gained attention in neuroscience and particularly in
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convolutional neural network (CNN ) architecture
CNN
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non-invasive
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The model structure is motivated by a state-of-the-art
Our CNN design is based on a
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MEG signal and is thus
electromagnetic (EEG and MEG) brain signals and is
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demonstrate
show here
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model is highly accurate in decoding
network is able to decode
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, and is robust with respect to inter-individual differences
and that it outperforms more complex neural networks and traditional classifiers used in the field
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generalizes well across users: when trained on data pooled from previous users, it can successfully perform on new users. Thus, the time-consuming BCI calibration can be omitted. Moreover, the model can be incrementally updated, resulting in +8.9\% average accuracy improvement in offline experiments and +17.0\% in a real-time BCI. We argue that this model can be used in practical BCIs and basic neuroscience research
is robust to inter-individual differences and can successfully generalize to new subjects in offline and online classification