The association between cross-frequency coupling and neuroplasticity via paired associative stimulation: TMS-EEG study

Paralleling insulated-gate bipolar transistors in the H-bridge structure to reduce current stress

In this study we present the new power electronic circuit implementation to create the arbitrary near-rectangular electromagnetic pulse. To this end, we develop a parallel- Insulated-gate bipolar transistors (IGBT)-based magnetic pulse generator utilizing the H-bridge architecture. This approach effectively reduces the current stress on the power switches while maintaining a simple structure using a single DC source and energy storage capacitor. Experimental results from the circuit characterization show that the proposed circuit is capable of repeatedly generating near-rectangular magnetic pulses and enables the generation of configurable and stable magnetic pulses without causing excessive device stresses. The introduced device enables the production of near-rectangular pulse trains for modulated magnetic stimuli. The maximum positive pulse width in the proposed neurostimulator is up to 600 µs, which is adjustable by the operator at the step resolution of 10 µs. The maximum transferred energy to the treatment coil was measured to be 100.4 J. The proposed transcranial magnetic stimulator (TMS) device enables more flexible magnetic stimulus shaping by H-bridge architecture and parallel IGBTs, which can effectively mitigate the current stress on power switches for repetitive treatment protocols.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42452-021-04420-y.

On the use of pairwise distance learning for brain signal classification with limited observations

The increasing access to brain signal data using electroencephalography creates new opportunities to study electrophysiological brain activity and perform ambulatory diagnoses of neurological disorders. This work proposes a pairwise distance learning approach for schizophrenia classification relying on the spectral properties of the signal. To be able to handle clinical trials with a limited number of observations (i.e. case and/or control individuals), we propose a Siamese neural network architecture to learn a discriminative feature space from pairwise combinations of observations per channel. In this way, the multivariate order of the signal is used as a form of data augmentation, further supporting the network generalization ability. Convolutional layers with parameters learned under a cosine contrastive loss are proposed to adequately explore spectral images derived from the brain signal. The proposed approach for schizophrenia diagnostic was tested on reference clinical trial data under resting-state protocol, achieving 0.95 ± 0.05 accuracy, 0.98 ± 0.02 sensitivity and 0.92 ± 0.07 specificity. Results show that the features extracted using the proposed neural network are remarkably superior than baselines to diagnose schizophrenia (+20pp in accuracy and sensitivity), suggesting the existence of non-trivial electrophysiological brain patterns able to capture discriminative neuroplasticity profiles among individuals. The code is available on Github: https://github.com/DCalhas/siamese_schizophrenia_eeg.