The organization of behavior: A neuropsychological theory

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.

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

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Art of electronics

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A technical guide to tDCS, and related non-invasive brain stimulation tools

Low intensity transcranial electric stimulation: Safety, ethical, legal regulatory and application guidelines

Brain-computer interfaces (BCI) enable paralyzed patients to communicate; however, up to date, no creative expression was possible. The current study investigated the accuracy and user friendliness of P300-Brain Painting, a new BCI-application developed to paint pictures using brain activity only. Two different versions of the P300-Brain Painting application were tested: A coloured matrix tested by a group of ALS-patients (n = 3) and healthy participants (n = 10), and a black & white matrix tested by healthy participants (n = 10). The three ALS-patients achieved high accuracies; two of them reaching above 89% accuracy. In healthy subjects, a comparison between the P300-Brain Painting application (coloured matrix) and the P300-Spelling application revealed significantly lower accuracy and P300 amplitudes for the P300-Brain Painting application. This drop in accuracy and P300 amplitudes was not found when comparing the P300-Spelling application to an adapted, black & white matrix of the P300-Brain Painting application. By employing a black and white matrix, the accuracy of the P300-Brain Painting application was significantly enhanced and reached the accuracy of the P300-Spelling application. ALS patients greatly enjoyed P300-Brain Painting and were able to use the application with the same accuracy as healthy subjects. P300-Brain Painting enables paralyzed patients to express themselves creatively and to participate in the prolific society through exhibitions.

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Brain Painting: First Evaluation of a New Brain–Computer Interface Application with ALS-Patients and Healthy Volunteers

Background: The corticospinal excitability indexed by motor evoked potentials (MEPs) following transcranial magnet stimulation (TMS) of the sensorimotor cortex is characterized by large variability. The instantaneous phase of cortical oscillations at the time of the stimulation has been suggested as a possible source of this variability. To explore this hypothesis, a specific phase needs to be targeted by TMS pulses with high temporal precision. Objective: The aim of this feasibility study was to introduce a methodology capable of exploring the effects of phase-dependent stimulation by the concurrent application of alternating current stimulation (tACS) and TMS. Method: We applied online calibration and closed-loop TMS to target four specific phases (0°, 90°, 180°, 270°) of simultaneous 20 Hz tACS over the primary motor cortex (M1) of seven healthy subjects. Result: The integrated stimulation system was capable of hitting the target phase with high precision (SD ± 2.05 ms, i.e. ± 14.45°) inducing phase-dependent MEP modulation with a phase lag (CI95% = - 40.37° to - 99.61°) which was stable across subjects (p=0.001). Conclusion: The combination of different neuromodulation techniques facilitates highly specific brain state-dependent stimulation, and may constitute a valuable tool for exploring the physiological and therapeutic effect of phase-dependent stimulation, e.g. in the context of neurorehabilitation.

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Valerio Raco

Papers

Brain Painting: First Evaluation of a New Brain–Computer Interface Application with ALS-Patients and Healthy Volunteers

Combining TMS and tACS for Closed-Loop Phase-Dependent Modulation of Corticospinal Excitability: A Feasibility Study.

Neurosensory Effects of Transcranial Alternating Current Stimulation

Closed-loop adaptation of neurofeedback based on mental effort facilitates reinforcement learning of brain self-regulation.

Cumulative effects of single TMS pulses during beta-tACS are stimulation intensity-dependent