Deep learning with convolutional neural networks for EEG decoding and visualization.

Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end-to-end EEG analysis, but a better understanding of how to design and train ConvNets for end-to-end EEG decoding and how to visualize the informative EEG features the ConvNets learn is still needed. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed tasks from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching at least as good performance as the widely used filter bank common spatial patterns (FBCSP) algorithm (mean decoding accuracies 82.1% FBCSP, 84.0% deep ConvNets). While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta, and high gamma frequencies, and proved useful for spatially mapping the learned features by revealing the topography of the causal contributions of features in different frequency bands to the decoding decision. Our study thus shows how to design and train ConvNets to decode task-related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG-based brain mapping. Hum Brain Mapp 38:5391-5420, 2017. © 2017 Wiley Periodicals, Inc.

Visualization of the amygdalo-hippocampal border and its structural variability by 7T and 3T magnetic resonance imaging.

The amygdala and the hippocampus are two adjacent structures in the medial temporal lobe that have been broadly investigated in functional and structural neuroimaging due to their central importance in sensory perception, emotion, and memory. Exact demarcation of the amygdalo-hippocampal border (AHB) is, however, difficult in conventional structural imaging. Recent evidence suggests that, due to this difficulty, functional activation sites with high probability of being located in the hippocampus may erroneously be assigned to the amygdala, and vice versa. In the present study, we investigated the potential of ultra-high-field magnetic resonance imaging (MRI) in single sessions for detecting the AHB in humans. We show for the first time the detailed structure of the AHB as it can be visualized in T1-weighted 7T in vivo images at 0.5-mm(3) isotropic resolution. Compared to data acquired at 3T, 7T images revealed considerably more structural detail in the AHB region. Thus, we observed a striking inter-hemispheric and interindividual variability of the exact anatomical configuration of the AHB that points to the necessity of individual imaging of the AHB as a prerequisite for accurate anatomical assignment in this region. The findings of the present study demonstrate the usefulness of ultra-high-field structural MRI to resolve anatomical ambiguities of the human AHB. Highly accurate morphometric and functional investigations in this region at 7T may allow addressing such hitherto unexplored issues as whether the structural configuration of the AHB is related to functional differences in amygdalo-hippocampal interaction.

Electrical Stimulation of the Human Cerebral Cortex by Extracranial Muscle Activity: Effect Quantification With Intracranial EEG and FEM Simulations.

OBJECTIVE: Electric fields (EF) of approx. 0.2 V/m have been shown to be sufficiently strong to both modulate neuronal activity in the cerebral cortex and have measurable effects on cognitive performance. We hypothesized that the EF caused by the electrical activity of extracranial muscles during natural chewing may reach similar strength in the cerebral cortex and hence might act as an endogenous modality of brain stimulation. Here, we present first steps toward validating this hypothesis. METHODS: Using a realistic volume conductor head model of an epilepsy patient having undergone intracranial electrode placement and utilizing simultaneous intracranial and extracranial electrical recordings during chewing, we derive predictions about the chewing-related cortical EF strength to be expected in healthy individuals. RESULTS: We find that in the region of the temporal poles, the expected EF strength may reach amplitudes in the order of 0.1-1 V/m. CONCLUSION: The cortical EF caused by natural chewing could be large enough to modulate ongoing neural activity in the cerebral cortex and influence cognitive performance. SIGNIFICANCE: The present study lends first support for the assumption that extracranial muscle activity might represent an endogenous source of electrical brain stimulation. This offers a new potential explanation for the puzzling effects of gum chewing on cognition, which have been repeatedly reported in the literature.